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Career Path: How to Become a Data Scientist

Data Scientists continue to be an in-demand and ever-evolving profession as they are often the key to solving operational issues within a business. When a company needs a major data modeling project completed within a given span of time, it is the Data Scientists who will research, develop and deliver this project. As businesses in every sector continue to rely on emerging technologies and an advancing scope of information to stay relevant and ahead of the competition, so Data Scientists will be needed to make sense of collected data and what it means to the present and the future of...more

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Opportunities for Data Scientists

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Career-building content for Data Scientists

We've collected tons of information on salaries, compensation, negotiation and more. See even more on our blog.

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Career Path: How to Become a Data Scientist

Data Scientists continue to be an in-demand and ever-evolving profession as they are often the key to solving operational issues within a business. When a company needs a major data modeling project completed within a given span of time, it is the Data Scientists who will research, develop and deliver this project. As businesses in every sector continue to rely on emerging technologies and an advancing scope of information to stay relevant and ahead of the competition, so Data Scientists will be needed to make sense of collected data and what it means to the present and the future of the company.

Getting Through the Door

Gaining an entry-level Data Science position often requires a certain level of educational experience like with many other technical jobs. This may include starting with a bachelor’s degree in IT, computer science, mathematics or related fields and moving on to the successful completion of a master’s program. Many companies prefer their Data Scientists to have a Ph.D., as it demonstrates the level of commitment, curiosity and determination a potential candidate possesses and the amount of work they have already put in. The degree level can also illustrate how experienced the Data Scientist is when using common technologies used daily, including Tableau, SPSS and Stata.

Along with educational experience, work experience is also key. Some Data Scientists started with a career in business, in science or in healthcare, mining, collecting and examining data just as they intend to do as a Data Scientist. Others have attained experience through relevant internships and other onsite job training, which can show a future employer you know what to expect on the job and can handle realistic situations like a pro.

Building a reputation through a mix of educational and work experiences can help set you apart from your competitors and help your desired company see you like the fittest candidate as a Data Scientist.

Degrees and Experience

Data Scientists typically begin their career in computer science and can evolve out of several areas from there. These tech-savvy pros often have an interest and possibly college degrees in statistics, econometrics, business and mathematics. Completing a Ph.D. program in computer science or a related field is often necessary for Data Scientists in many companies. Before moving into the Data Scientist field, starting careers for these individuals have been as diverse as biologists, database administrators, physicists and web developers; as long as the professional has an interest in facts and figures and using them to solve problems, then this career can be exceedingly appealing.

Working as a Junior Level Data Scientist

Data Scientists at the junior level often spend their days examining and processing data that helps paint a clear picture of how a project can be completed or a business problem can be solved. They often work with other data experts as well as business managers, marketing teams and others depending on the task and are expected to work effectively with others as well as alone. Being detail oriented and having excellent critical thinking skills will help you through your daily duties. Leveraging technologies like Java, Python, R and SAS to program and manage databases and manipulate data for analysis will be required.

Moving up the Ranks

To effectively ascend the levels in the Data Scientist field, these professionals need to demonstrate that they are working well on assigned projects, picking up technical skills as needed and standing out from their colleagues. If it is the Data Scientists goal to achieve senior status, successfully completing work goals is only part of the process. Going the distance when it comes to tasks is helpful. If a co-worker seems overwhelmed and you can help, doing so displays great leadership skills, which is how most Data Scientists move forward. This as well as keeping an eye on industry trends is useful, as it demonstrates that you are forward-thinking and capable evolving. Networking is another great way for a Data Scientist to rise in the ranks, as they may learn from like-minded career Data Scientists various tips and tricks that prove useful.

Advance Your Career: How to become a Senior Data Scientist

Senior Data Scientists have worked professionally in the field for a number of years and often have completed a Ph.D. program. Attaining a senior level of this profession means Data Scientists must have a portfolio filled with accomplishments they have attained so far. Maybe they worked on a team that was tasked with developing a new machine learning program that can handle large datasets and this individual put forward their own ideas that helped make the project a success. Proving results like this speaks highly of the Data Scientist to their ranking seniors and managers. It is this mix of education, experience and results that can get a Data Scientist to a high-paying and respected senior position.

Study the Core Fields

Becoming a Data Science expert involves the understanding and utilization of many key components. It is a highly-technical field, so being familiar with the programs, languages and tools needed to successfully accomplish work goals is ideal. Data Scientists have often taken computer science and understand tech basics, building their technical skillset as time goes on. Having a creative mind and knowing how to express that creativity with particular software is also important, as Data Scientists frequently develop graphics to help explain big data figures. Because they often meet with clients and other business leaders, understanding modern business practices can also be beneficial to the success of a Data Scientist.

Invest in Yourself

As your career progresses, be sure to take any opportunity available to develop your skills. Conferences, online courses and time spent in other departments and even in different companies are all great ways to advance your career as a Data Scientist.

Don’t Stop at Data Science

The skills Data Scientists master and utilize daily makes them experts when it comes to retrieving useful data, uncovering patterns and clues to certain behaviors and how best to move forward with this knowledge at hand. Specializing in statistics, business operations or human behavior can prepare a Data Scientist to engage with other fields and companies that complement their particular focus and interests. Both honing and expanding your Data Scientist skills and keeping up with industry trends can keep you on the path to success throughout your career.

Data Scientist Job Description

Data Scientists take data meaningful to a company and turn it into usable information that helps the company make better operational decisions. Utilizing a variety of tools and advanced software, Data Scientists examine data to discover patterns and use it to tell a story. It is also a highly-communicative role, as Data Scientists not only mine for and manipulate data but must also create visuals and present their findings to business leaders, colleagues and clients. Being able to describe a journey from A to B is key to success in this role.

It is not only a role that has remained in-demand with employers, but it is one with excellent room for growth. Data Scientists can move from entry to junior, to senior levels within only a couple to a few years, depending on their particular journey, with pay raises that match this level of advancement.

What We Need Your Help With

  • Create predictable data models with the use of machine learning algorithms.
  • Taking data that has been minimally processed and analyze its meaning.
  • Discover data patterns and what they mean to the business
  • Use tools and programs to develop eye-catching and straightforward visuals that help illustrate your message.
  • Developing better means and methods of data collection.

We Look For

  • The ability to create data models using languages like Python, R, SPSS, Stata, SAS and Julia.
  • Aptitude with tools like Tableau, Rapidmeter and Excel as well as languages like Scala, Java and C#.
  • Familiarity with packages like Scitkit-Learn, NumPy and Matplotlib to create data visuals.
  • The ability to tell your data findings in an interesting way.

These Would Also Be Nice

  • Good communication skills
  • Being highly analytical
  • Having a desire to solve problems
  • Balancing logical and creative mindsets

Senior Data Scientist Career Paths: Where to Go from Here

Data Scientists can enjoy a long and varied career at the senior level and they can also move on to other departments and fields. Because a successful senior Data Scientist can foretell a business’s needs after achieving so much experience, they may move on to become Data or Business Operations Managers, charged with overseeing all aspects of a company’s data retrieval or processes.

Considering your interests as you move through your Data Science career can help you decide what path to take. This is important, as you can start working towards that goal, even if it isn’t in Data Science, as you move through the field. Having an aptitude for analytics, statistics, tech and more means the world is your oyster. Keep your skills sharp and your eye on trends and you can go anywhere.


Structured Query Language (SQL) is a highly popular domain specific language (DSL) used to communicate with relational database management systems (RDBMS). SQL is a standard that is based on the "relational model", defined by professor E.F. Codd,...

R is a language designed for data manipulation and visualization. It is capable of doing various statistical computing and graphic generation (including linear and nonlinear modelling, classical statistical tests, time-series analysis, classificat...

Python is an object-oriented programming language notable for its clarity, power and flexibility. Python is an interpreted language, meaning that an interpreter reads and runs the code directly, rather than compiling down into static lower level c...

Java is a statically-typed, cross-platform language. It is concurrent, class-based, and object-oriented. It has minimal implementation dependencies and compiled Java code can run on all platforms that support Java without the need for recompilat...


C++ is an object-oriented language derived from C, and invented by Bjarne Stroustrup, while working at AT&T's Bell Labs. It is widely used for systems-level programming, and building applications on Windows and various Unix operating systems (Lin...

Scala is a strongly, statically typed functional/object-oriented language primarily targeted to the JVM. It brings a lot of powerful programming language concepts out of academia, placing them into the hands of practitioners. A key design emphas...


C is a widely used low-level, static-typed, compiled computer language known for its efficiency. Developed in the late sixties, C has become one of the most widely used languages of all time. It provides direct access to memory and due to its de...

"Agile Methodologies" or "Agile" are ways of describing an iterative approach to software development. Often adopted as an alternative to waterfall and other traditional sequential development practices. It is meant to help teams work quickly to i...

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Top industries hiring Data Scientists


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Analytics and Business Intelligence roles are often confused but are also directly linked. Data that is collected when a user interacts with a system is then cleaned and stored. That data is then accessed using reports and graphical dashboards. Th...


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