Data Science Jobs

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For four years in a row, data scientist has been named the number one job in the U.S. by Glassdoor.

Also, the U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026.

Not only is there a huge demand, but there is also a noticeable shortage of qualified data scientists.

Daniel Gutierrez, the managing editor of insideBIGDATA, told Forbes, “The word on the street is there’s definitely a shortage of people who can do data science.”

If you have a passion for computers, math, and discovering answers through data analysis, then earning an advanced degree in data science or data analytics might be your next step.

What Is Data Science?
How to Become a Data Scientist
What Are the Jobs for Data Scientist?
How Can a Data Scientist Make a Lot Money?
How can I Make Money From Data?
How Much do Freelance Data Scientists Make?
Can Data Scientist Freelance?
How do Individual Data Scientists Earn Money?
How Much Does a Freelance Data Scientist Earn Per Month?
Can Anyone be a Data Scientist by Taking Classes Online?
Freelance Websites for Data Scientists
Career Advice to Data Scientist
Career Options for Data Experts

What Is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract value from data. Data scientists combine a range of skills—including statistics, computer science, and business knowledge—to analyze data collected from the web, smartphones, customers, sensors, and other sources.

Read Also: A Software Developers Career Guide

Data science reveals trends and produces insights that businesses can use to make better decisions and create more innovative products and services.

Data is the bedrock of innovation, but its value comes from the information data scientists can glean from it and then act upon.

How to Become a Data Scientist

Find out if it’s really for you

Both intellectually and in terms of education, data science is a heavy lift. Consider taking a free data science course through an online learning portal like EdX to make sure this field is really right for you before you take the plunge.

Choose an academic path

According to a study from Burtch Works Executive Recruiting, it’s nearly impossible to attain the skills needed for a job in the field without earning a high-level degree, which 9 out of 10 data scientists have done.

Although more and more data scientists are opting for master’s degrees, about one in four professionals in the field with less than three years experience still hold PhD. degrees.

In total, 44 percent of all working data scientists have earned a PhD. It’s important to note, however, that you can mitigate the commitment and cost of your education by participating in a variety of Internet-based massive open online courses (MOOCs). Data science “bootcamps” can also speed things up with accelerated, concentrated courses of instruction.

Choose an area of concentration

Many different paths can lead you to a lucrative, rewarding career as a data scientist. Most start at the undergraduate level, with bachelor’s degrees in data science that can lead to jobs like data visualization specialists, management analyst and market research analyst.

From there, many students go on to achieve master’s degrees in fields like machine learning algorithm developer, statistician or data engineer. Many students then pursue doctorate degrees in concentrations such as business solutions scientist, data scientist, and enterprise science analytics manager.

Get certified

Earning a certification can improve your skills and make you a more marketable candidate. Potential certifications include certified applications professional, Cloudera certified professional: data scientist, EMC: data science associate and SAS certified predictive modeler using SAS Enterprise Miner 7.

Get hired

Once you’ve emerged from your education pursuits, the only thing you need in order to call yourself a data scientist is a job. Data science is a specialty, and recruiting networks, job boards and career forums exist just for them and the companies who need their skill sets.

Start with Kaggle, which hosts 1.5 million data scientists in the world’s largest community dedicated to the profession. Businesses and other entities constantly search Kaggle for people who have the skills and background they need. iCrunchData is another good place to network and start your job search.

What Are the Jobs for Data Scientist?

1. Data Analyst

Data analysts are responsible for a variety of tasks including visualisation, munging, and processing of massive amounts of data. They also have to perform queries on the databases from time to time.

One of the most important skills of a data analyst is optimization. This is because they have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.

2. Data Engineers

Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.

3. Database Administrator

The job profile of a database administrator is pretty much self-explanatory- they are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements. They are also responsible for database backups and recoveries.

4. Machine Learning Engineer

Machine learning engineers are in high demand today. However, the job profile comes with its challenges. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc. machine learning engineers are also expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.

5. Data Scientist

Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing.

For instance, they are expected to perform predictive analysis and run a fine-toothed comb through an “unstructured/disorganized” data to offer actionable insights. They can also do this by identifying trends and patterns that can help the companies in making better decisions.

6. Data Architect

A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also ensure that the data engineers have the best tools and systems to work with.

7. Statistician

A statistician, as the name suggests, has a sound understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.

8. Business Analyst

The role of business analysts is slightly different than other data science jobs. While they do have a good understanding of how data-oriented technologies work and how to handle large volumes of data, they also separate the high-value data from the low-value data. In other words, they identify how the Big Data can be linked to actionable business insights for business growth.

9. Data and Analytics Manager

A data and analytics manager oversees the data science operations and assigns the duties to their team according to skills and expertise. Their strengths should include technologies like SAS, R, SQL, etc. and of course management.

How Can a Data Scientist Make a Lot Money?

There’s no objective way to define “best”, as it varies by person. We have provided some steps through each way to make money as a data scientist.

Get a data science job

This can involve a significant investment in learning and interviewing. It often takes 1–2 years to learn enough data science to get one of the more desirable data science jobs.

The monetary rewards can be high, but in most cases will be around what a top-tier software engineer makes.

This can be an easy option if you land at a large company with low expectations, but can be very hard if you’re in a high visibility position.

There often isn’t a lot of control over your work, although this varies by company. In a smaller startup you might be working longer hours, but in a bigger company, you might be doing less interesting work.

This is the option most people choose, and it’s a good default.

Consult on data science projects

This is more difficult than getting a data science job, simply because you have to learn, then put in a lot of work to build your profile and authority. It’s also a lot of work to constantly build your portfolio and gather good reviews so you can up your rates.

Although the eventual financial rewards can be high, they’re about on par with the top-paying data science jobs. The big advantage here is more control and freedom.

You can pick your clients and set your hours. The downside is that clients may expect ongoing maintenance, and you’ll have to constantly manage existing clients while finding new ones.

This is a good option after you’ve had a job, and have a network of contacts who you can ask for consulting work.

Build a tool for external consumption that leverages data science

This generally manifests as starting a company. An example would be a tool that analyzes a company’s website traffic, and tells them what to optimize on their site. Your goal would be to charge for this tool, and get revenue.

The initial effort is very high, and you won’t be paid a lot. You’ll probably want some money saved away before doing this. Although the eventual rewards can be high, it’s no guarantee, and it can take years.

The benefit is that you get a lot of control over what you’re doing, and you get to build your vision. Even still, you’re still accountable to customers.

This is a good option once you’ve had a data science job, and have a good idea of the problems in the industry.

Build a tool for your own consumption that leverages data science

An example of this would be creating a tool that automatically buys stocks low and sells high, or a tool that tells you what houses to buy so you can flip them for a profit. This can be a nice way to make money, particularly if you find a good niche.

It can be very hard to identify that niche, though, so it usually takes a lot of effort to find and tweak. It also requires a good amount of upfront money, as you’ll usually need to spend money upfront, then see it returned later.

There is a lot of control if you choose this option. As long as you’re making enough money, you aren’t accountable to anyone, and can do whatever you want with your time.

This is a good option once you have some money saved, and understand problems that could be solved with data science.

Teach

In order to teach, you’ll need to build up authority and credibility, so you’ll probably need to have a job or consult beforehand.

It also has a lot of the upfront risks of a startup in that you won’t make much money for a while, as you refine your curriculum, and find the right audience.

There is a good amount of control here, as you choose how you teach, but you’re also accountable to students, and want to see them succeed.

We recommend this after you have a data science job, and only if you enjoy teaching.

There are quite a few ways to make money with data science, but they all involve good amounts of time investment, both upfront and ongoing. I’d think hard about what kind of lifestyle and income you want, then pick accordingly.

How can I Make Money From Data?

There are several ways you can sell your data, including selling it to another company directly or joining a marketplace. You can sell your data itself or sell the insights you gain from it.

Sell your data Directly

The most straightforward method is to sell your data directly to another organization through a private interaction that either you or the other party sets up.

This approach requires you to either already have a relationship with a company that might want to buy your data, or to do some research and find a potential buyer.

Join a Private Marketplace

You can also join a private data marketplace where companies exchange data. On the Lotame Private Data Exchange™ (PDX), you can easily sell your data to high-end buyers in transparent transactions.

With PDX, there’s a steady stream of buyers readily available whom you likely would not have otherwise connected with.

Sell your data to a data Aggregator

Another option is to sell your data to a data aggregator or another similar company that will then sell your data to companies that will use it.

Going through a third party is an easy way to sell a lot of data fast, but you don’t have as much control over the transaction, and you may not get as favorable of a price.

If you decide to go this route, be sure that you do some research to ensure the company you work with is reputable, secure and capable.

You also have several options related to the form in which you sell your data. You can sell the data itself, or you can share insights you gained by analyzing your data.

You can sell the results of analyses you already performed or, if you have resources and personnel to do it, offer to use your data to solve specific problems and produce custom insights.

One benefit of selling insights rather than data is that you maintain sole control over your data.

How Much do Freelance Data Scientists Make?

As of Aug 2020, the average annual pay for a Freelance Data Scientist in the United States is $100,349 a year.

While ZipRecruiter is seeing annual salaries as high as $182,000 and as low as $22,500, the majority of Freelance Data Scientist salaries currently range between $52,000 (25th percentile) to $140,500 (75th percentile) across the United States.

The average pay range for a Freelance Data Scientist varies modestly (up to $88,500), which suggests there may be fewer opportunities for advancement based on skill level, but increased pay based on location and years of experience is still possible.

Based on recent job postings on ZipRecruiter, the Freelance Data Scientist job market in both Lagos, NG and the surrounding area is very active.

People working as a Freelance Data Scientist in your area are making on average $100,349 per year or the same as the national average annual salary of $100,349. ranks number 1 out of 50 states nationwide for Freelance Data Scientist salaries.

To estimate the most accurate annual salary range for Freelance Data Scientist jobs, ZipRecruiter continuously scans its database of millions of active jobs published locally throughout America.

Can Data Scientist Freelance?

Are you a data scientist looking to kickstart your freelance career? Whether you’re a seasoned data scientist looking to take on side projects or a newly-minted data scientist forging into the world of freelance, we’ll break down the best way to market yourself and get clients.

There is plenty of uncertainty when you strike out on your own, but the following 5 tips will help you get started the right way:

1. Market yourself

When you first start as a freelance data scientist, be aggressive in your efforts to find work. Updating your LinkedIn or Indeed, then waiting for leads to pour in won’t cut it. Don’t assume work will come to you—you’ll need to find it.

First things first, create a personal website that showcases a variety of your work. Make sure your website is easy-to-navigate, clean, and up-to-date.

Potential clients will want to see an updated portfolio of your work, so make sure to refresh it constantly.

Second, don’t be afraid to network. Engage in chat forums and in-person meet-ups, ask about available work, ask about clients seeking freelancers. Even if no clients have projects for you currently, that doesn’t mean they won’t call you once they do.

2. Get on the right platform

Get involved with freelance sites like Upwork or Toptal. Both are free to use and user-friendly, and creating an account is intuitive.

There are multiple websites you can turn to if you want to increase your odds of getting a gig or dabble in a variety of work.

Data Science Stack Exchange is another excellent resource. It’s a Q&A chat forum for data scientists to ask questions, share best practices, and swap codes.

It’s also worth checking out Kaggle, another popular online chat forum. Kaggle hosts data science competitions with high-priced payouts for winners.

3. Research how others do it

Study successful freelance data scientists that get hired. Understand what rockstar data scientists are doing to land jobs and do them right.

For example, most data scientists are highly educated and have postgraduate degrees. In order to hone the bevy of skills required to be data scientists, you should be well-versed in at least Computer Science, Mathematics, Statistics, Physical Sciences, or Social Sciences.

In freelance work, specifically, the more work experience you have, the better. And the more experience and education you have under your belt, the higher the rate you can set.

4. Determine your rate

Some clients will charge $150–$200 per hour while others charge only $45-$100 per hour.

Before you begin any project, determine your net worth.

When you’re deciding your rate, never lowball yourself out of desperation to get a job. There’s high demand for data scientists, so there’s a slew of work available—and a host of employers willing to pay big.

If you have a postgraduate diploma, you shouldn’t ever freelance for less than $80 a job. Most experienced freelancers charge anywhere between $100 and $250, depending on the project.

Here are some high-paying tools that can up your rate:

Scala
Spark
D3
Amazon Elastic Mapreduce

It’s essential to build up your arsenal of skills. For instance, knowing how to speak Scala can add $15,000 to your salary. Having this skill as a feather in your cap is a sure-fire way to increase your rate.

Steer away from proprietary source tools and focus on open source tools. If you can, try to have 15+ tools under your belt (vs. 10 or 12). It also doesn’t hurt to get familiar with cloud computing and Python.

Last but not least, don’t be afraid to negotiate. Your skills are in high demand, and if a company makes $300,000 from your project, then be confident and ask for what you deserve—no matter how long you spent on the project.

5. Establish a work station

Find a quiet workplace that will allow you to be as productive as possible. If this means working at a coffee shop, budget for the $5 you’ll spend on coffee each day.

As you’re starting, it may make sense to save money and work from home—especially if freelancing is your only source of income.

If you’re working from home, dedicate a working-only space. That way, you can be as productive as possible and separate your work life from your home life.

Above all, before you begin freelancing, check that you have reliable internet speed. Your business hinges on your ability to be online.

Make sure you have an internet connection that won’t slow you down. Plus, when you’re freelancing, you can’t call on IT to clear your cache or reboot your Wi-Fi.

Below are 9 ways you can make extra income as a data scientist. These ways vary in difficulty, risk level, and the amount of money that you can make, but all of them have the potential to help you develop your skills and earn extra income.

1) Write for medium or start your own blog.

2) Complete in kaggle or topcoder competitions.

3) Work as a data science contractor.

4) Create a web-based product and generate traffic.

5) Create a sports betting or daily fantasy advantage.

6) Tutor or teach a class at a university.

7) Apply your data science skills to finance
Robinhood: https://join.robinhood.com/kennetj785

8) Aggregate data and sell it through an API

9) Start a YouTube Channel

How do Individual Data Scientists Earn Money?

1. Collect Data Set and sell

You know data science is built on the top of under lying data . If you are not good in data science coding stuffs, You may start with Collect Data Set and sell. You may sell your data set to popular website like kaggle .

You may earn money from data science by doing this good contribution to data science community.

2. Create a blog or online e-learning website

Data science is new. Like every other technologies we love to read the content on e-learning websites. The opportunity is for you that there are very less resources in data science.

Whatever already present is good but data science fields lacks some more quality contents. Like we are doing in data science learner, we are trying to create interesting and valuable content for you and people like you.

The best part is we are also earning although its my passion. There are so many ways to monetize blog like Adsense, affiliate, digital courses etc. You may start to earn money from data science in this way as well.

3. Start a consulting firm on data science

You may already know about it . You may start with small projects with clear define goals. Our suggestion is to save your self from the projects which involves too much R&D in consulting projects.

R&D in data science is always should be in bound. The client should pay R&D scientist engineers on their own pay roll. Because It is rally harder to justing the pay for R&D Engineers.

Think about it you make a contract a project without PoCs and suppose some part is blocking the project which is matter of research then you may get stuck.

So try to consider only those project while consulting which you already done in before or similar to them.

4. Develop a small product on data Science

When it comes to Product and Product Development .People always think product is always a big stuff. Its not really true, Products are something which at-least solve a real problem.

Some time this misconception occurs because of the product we use in daily life are like a tree. For example you use Google and find its combination of multiple app like searching console, YouTube, google plus etc.

Actually all great products are start from a single unique value offering feature. Even if you read books like Lean startups you will get the same advice.

The best way is to develop a feature and launch it, get the feed back, review it and improve it. Once this cycle is done develop the second and so on the top of it.

How Much Does a Freelance Data Scientist Earn Per Month?

According to Glassdoor, the average data scientist salary is $113,000, with a range of $85,000 to $170,000. Data analysts earn less at the entry level, from $50,000 to $75,000.

Big data engineer salaries usually start at $70,000 and can increase up to $165,000 for a domain expert. Over time, top freelancers can earn more on a monthly or annual basis than W2 workers.

On Upwork, freelance data scientists earn a wide range: $36 to $200 per hour or $400 per project.

Many projects may be completing ad hoc analyses or queries, building a data pipeline, or creating an engine to generate data-driven recommendations.

If you know more specific languages, like Scala, Spark, and Hadoop, you can charge higher rates. Your rates should increase as you gain knowledge, experience, and more specialized skills.

On Fiverr, data analysis services start at $5 to $10 and go up to $100 to $200 for more complex services like geospatial data analysis with visualization and machine learning/deep learning with computer vision.

So what exactly should you charge? As mentioned earlier, your earnings as a freelancer are entirely up to you. Charge as much as you feel comfortable with charging, based on your needs and the relevant data you collect.

Can Anyone be a Data Scientist by Taking Classes Online?

Data Science is not that small a topic that one can learn by taking a few online classes. It is vast field and it takes quite an amount of time to master.

Taking up such classes online may look good on your resume and may give you a little advantage over others but landing a full-time Data Science job is very unlikely. You may get some beginner level internships offers though.

Experience in Data Science field is essential. Appropriate degrees and certifications usually supplement the experience.

Above all, one must have a deep desire and interest to make a career in Data Science. It is because without an interest in this field, you won’t last a long.

One of the better options is to enrol in Post Graduate Diploma course. The usual duration is about 11 months but it is a great investment. The institutions or academies offering the post graduate courses provide you placement assistance as well.

You can consider Upgrad, Udemy or Manipal ProLearn to enrol with for your PG courses.

Freelance Websites for Data Scientists

1. Toptal

Toptal is the premier source for elite freelancers. Why? Unlike a lot of other freelance marketplaces, Toptal accepts only the top 3% of the freelance data science engineers.

Applicants are tested in various areas, including English language skills and technical ability.

If you’re open to hiring remote freelancers, then Toptal is the clear answer for your company. Scaling through Toptal is simple, trying their service is risk-free, and freelancers are matched quickly (oftentimes in less than 48 hours).

2. iCrunchData

iCrunchData is a data science job board that caters to employers seeking top data science engineers for hire.

If you are confident that your company will be able to vet, interview, and hire without the assistance of a freelance marketplace, recruiting service, or technical hiring agency, then you will definitely want to post your job listing on iCrunchData.

It’s solely dedicated to data science professionals, making it a perfect place to begin your search for a data science engineer.

3. Stack Overflow

Stack Overflow is another niche developer site like GitHub. Stack Overflow is used primarily as a resource for programmers, techies, and developers looking for answers to technical questions. Coders of all skill levels post answers to pressing coding questions.

As one of the largest and most trusted developer communities on the web, Stack Overflow’s job board is a spectacular site to search and hire data science engineers. With over 50 million visitors every month, your job posting is sure to get the exposure it needs.

4. Open Data Science Jobs

Open Data Science Jobs is one of the largest data science-only job boards. At the time of this writing, Open Data Science Jobs has 126 job postings by 66 companies.

Large companies like Bose have utilized the job board to find the best data scientists. If you’re looking to hire data science engineers yourself, then you may want to consider leveraging Open Data Science’s popular job board.

5. Kaggle

Kaggle is the largest community of data science engineers, data scientists, and analytics professionals. Kaggle provides data sets, hosts competitions, and fosters community through message boards and learning resources.

As such, Kaggle’s job board is one of the best sites to begin your search for top data science engineers and has been utilized by some of the largest companies (Amazon, Capital One, and AIG — just to name a few).

6. GitHub Jobs

Niche job boards are often the best places to begin your search for data science engineers. Using general job boards like Monster, Indeed, and Craigslist can eat up a significant portion of your time.

You can save time by going directly to developer communities like GitHub.

GitHub is the most popular code repository on the market today, attracting millions of independent developers and tech companies.

GitHub Jobs is the company’s job board and can be a great place to begin your search for dedicated data science engineers.

7. Scalable Path

If you need a team of data science engineers in a matter of a few short weeks, then Scalable Path is a wonderful service to employ.

While it cannot boast an elite engineer base as Toptal does, they do offer experienced data scientists who are more than qualified to help you with your latest project.

If you need a team assembled, Scalable Path is a great option. Otherwise, maybe look into freelance marketplaces like Toptal, Gigster, or Gun.io.

8. Gigster

Gigster has attracted some of the most prolific and respected investors. Why? Gigster is a premium recruiting platform that matches technical talent with important projects.

Entire teams can be assembled through Gigster, including a project management team member that can help lead the project and act as a liaison between Gigster’s team and your company.

Their freelancers have worked with esteemed institutions like Google, Microsoft, Y Combinator, and Stanford University so you can be absolutely certain that your project will be handled with the utmost care.

However, they do not offer a 48-hour guarantee. If you need to hire data science engineers in a pinch, try matching services like Toptal.

9. X-Team

X-Team is another wonderful freelance marketplace for technical teams. If you need to assemble an outsourced tech team, X-Team can be a tremendous option for businesses of all sizes.

However, it’s very important to note that X-Team is specifically tailored to creating a team of freelancers, replete the project managers, and not individual freelancers.

If you need an entire team of data science engineers, then X-Team is a great option. Otherwise, you might want to consider services like Toptal or Gun.io.

10. Gun.io

Gun.io is very similar to Toptal, matching companies with qualified and supported data science engineers swiftly and effectively. With over twenty-five thousand members, Gun.io is a rising star in the talent industry.

Although Gun.io does not provide as many guarantees as Toptal, it is a great option.

Career Advice to Data Scientist

Many people are interested in becoming Data Scientists, but it can be difficult to identify what you need to learn to land your first Data Science job.

These these career advice are for anyone who is starting their Data Science career with the goal of obtaining a job in industry and applying Machine Learning and Deep Learning to solve business problems.

Choose a focus area to master

The field of Data Science is still being developed and defined, and as a result, there will always be something new and crucial to learn.

It can feel daunting and frustrating to tackle learning the entire field of Data Science at once. We recommend developing a broad knowledge in all the methods and tools listed above, but also choose an area within Data Science to focus on and master.

For example, many Data Scientists make NLP or Deep Learning their focus. Pick something you really enjoy or something you could use to solve problems in a field that you’d like to work in.

Selecting a focus area allows you to truly master a small, digestible piece of the field while still maintaining your working knowledge of the entire field.

Becoming an expert in one area will build your confidence in the field, and can help you establish yourself as an experienced Data Scientist.

Talk about the stuff you don’t know as well as the stuff you do

As Data Scientists, it is important to recognize and discuss the things we don’t know, as well as the things we do.

If we have open conversations about topics that interest us that we’d like to learn more about and discuss mistakes we’ve learned from, we will be able to learn from each other and ultimately become better Data Scientists.

But if we pretend we’ve mastered everything and never make a mistake, we’ll make others feel inferior and discouraged, and will not be able to learn from those around us.

If you find yourself working in an environment where you feel like you can’t make a mistake, or can’t admit that you don’t know something, you’re in a toxic environment and you should probably start looking for your next job opportunity.

Study the business

Come to your interview with a list of ideas of how the company could use Data Science. We believe it is one of the key strategy that ultimately leads to a job offer.

Be energetic and positive in the interview

It’s easy to be serious and focused when you’re nervous, but it is important to still appear excited about your work.

If you can try to connect with your interviews and be conversational, you will make a good impression, and you may become less nervous as the interview will become more of a conversation and not an interrogation.

Treat interview questions as talking points

This is especially important in phone screens. Early in my career, I made the mistake of answering the questions with a simple sentence, or even just “yes” or “no”.

This was totally the wrong approach. Take the opportunity to explain your answers in detail, or even use the question to jump into something you want to talk about.

Have opinions about the field of Data Science

Do you have ideas on how Data Science should or shouldn’t be used? What makes a Data Science team successful? Have you seen something derail a project in the past? How should a Data Scientist even be defined?

These are all insights about your field that you could bring to the table. Because the field is so new, we all have the responsibility as Data Scientist to help define and clarify what a Data Scientist does.

Know everything on your resume

If it’s on your resume, its fair game for an interviewer to ask you to describe it. Don’t put anything on your resume that you can’t explain or remember. Before interviews, practice describing all the models you have used on past projects and anticipate direct questions related to these models.

Career Options for Data Experts

While data analyst, data scientist, and data engineer broadly describe the different roles data experts can play at a company, there are a variety of other job titles you’ll see that either relate directly to these roles or otherwise involve the use of data science skills.

Below, we’ll take a quick look at job titles you might want to consider when looking for employment.

Machine Learning Engineer

Average salary: $144,085

There is a lot of overlap between a machine learning engineer and a data scientist. At some companies, this title just means a data scientist who has specialized in machine learning.

At other companies, “machine learning engineer” is more of a software engineering role that involves taking a data scientist’s analysis and turning it into deployable software.

Although the specifics vary, virtually all machine learning engineer positions will require at least data science programming skills and a pretty advanced knowledge of machine learning techniques.

You may also see positions like this listed as “Machine Learning Specialist,” particularly if the company is looking for a data scientist who has specialized in machine learning rather than a software engineer who can build deployable products that make use of machine learning.

Quantitative Analyst

Average salary: $142,049

Quantitative analysts, sometimes called “quants”, use advanced statistical analyses to answer questions and make predictions related to finance and risk.

Needless to say, most data science programming skills are immensely useful for quantitative analysis, and a solid knowledge of statistics is fundamental to the field.

Understanding of machine learning models and how they can be applied to solve financial problems and predict markets is also increasingly common.

Data Warehouse Architect

Average salary: $136,151

Essentially, this is a speciality or sub-field within data engineering for folks who’d like to be in charge of a company’s data storage systems.

SQL skills are definitely going to be important for a role like this, although you’ll also need a solid command of other tech skills that’ll vary based on the employer’s tech stack.

You won’t be hired as a data warehouse architect solely on your data science skills, but the SQL skills and data management knowledge you’ll have from learning data science make it a position that should be on your radar if you’re interested in the data engineering side of the business.

Business Intelligence Analyst

Average salary: $90,150

A business analyst is essentially a data analyst who is focused on analyzing market and business trends.

This position sometimes requires familiarity with software-based data analysis tools (like Microsoft Power BI), but many data science skills are also crucial for business intelligence analyst positions, and many of these positions will also require Python or R programming skills.

Statistician

Average salary: $87,021

‘Statistician’ is what data scientists were called before the term ‘data scientist’ existed. Required skills can vary quite a bit by from job to job, but all of them will require a solid understanding of probability and statistics.

Programming skills, especially in a statistics-focused language like R, are likely to be of use as well. Unlike data scientists, a statistician will not typically be expected to know how to build and train machine learning models (although they may need to be familiar with the mathematical principles that underlie machine learning models).

Business Analyst

Average salary: $78,172

‘Business analyst’ is a pretty generic job title that’s applied to a wide variety of roles, but in the broadest terms, a business analyst helps companies answer questions and solve problems.

This doesn’t necessarily involve the use of data science skills, and some business analyst positions don’t require them.

But many business analyst jobs do require the analyst to capture, analyze, and make recommendations based on a company’s data, and having data skills would likely make you a more compelling candidate for almost any business analyst role.

Systems Analyst

Average salary: $73,574

Systems analysts are often tasked with identifying organizational problems, and then planning and overseeing the changes or new systems required to solve those problems.

This typically requires programming skill (although systems analysts are not always directly involved in developing the systems they recommend) and data analysis and statistical skills are also frequently necessary for identifying problematic trends and quantifying what’s working well and what isn’t within a company’s tech systems.

Marketing Analyst

Average salary: $66,470

Marketing analysts look at sales and marketing data to assess and improve the effectiveness of marketing campaigns.

In the digital age, these analysts have access to increasingly large amounts of data, particularly at companies that sell digital products, and while there are a variety of software solutions like Google Analytics that can allow for decent analysis without programming skills, an applicant with data science and statistics chops is likely to have a leg up on many other applicants if they also have sufficient domain knowledge in the area of marketing.

Plus, a marketing analyst whose analyses make a significant impact can set their long-term sights on a Chief Marketing Officer position, which pays an average of $157,960 per year.

Operations Analyst

Average salary: $62,468

Operations analysts are typically tasked with examining and streamlining a business’s internal operations.

Specific duties and salaries can vary widely, and not all operations analyst positions will make use of data skills, but in many cases, being able to clean, analyze, and visualize data will be important in determining what company systems are working smoothly and what areas might need improvement.

Conclusion

The easiest way to assess your own readiness is simply to start taking a look at real-world jobs and job descriptions. Do you have the skills that are listed there? Do you feel like you’d be able to do (or learn to do) the tasks described?

Your answer to these questions doesn’t have to be a rock-solid yes. Impostor syndrome is a real thing and particularly for entry-level applicants searching for their first data science job are particularly susceptible to feeling it.

It’s easy to look at an employer’s wish-list of skills and qualifications and intimidate yourself out of even applying.