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TDWI Upside - Where Data Means Business

Navigating Your Data Science Career

From undergraduate to working professional, these four milestones create a guide to a career in data science.

Data science is an ever-changing profession with rapid expansion across a multitude of industries and specializations. This growth can make entering the industry and finding the right career path challenging. Fortunately, there are numerous resources and opportunities to assist in navigating a data science career.

For Further Reading:

Cybersecurity Plus Data Science: The Career Path of the Future?

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Coming Soon to Analytics Teams: Analytics Translators

This article will focus on four key career milestones that can provide direction in your data science career journey. These milestones include preparing to enter the market, finding analytics passions, diversifying experience, and bridging business specializations.

Preparing to Enter the Data Science Market

In college many people assume that internships are the only path to gaining experience needed for finding a first job. Although internships are one of the best ways to gain experience and build a résumé, there are many other opportunities.

For example, co-op programs are a great resource for getting into the data science market. These programs tend to be full time, paid positions during a college semester that give hands-on company experience.

Another lesser-known opportunity that has grown for data science students is Research Experiences for Undergraduates (REU). This program supports active research participation by undergraduate students in any of the areas of research funded by the National Science Foundation. Students do not have to be enrolled at the university that is offering the REU, so many opportunities are available across the country.

Additionally, there are several websites and universities that offer online training and certifications that allow a student to gain knowledge on a data science specialty and add that achievement to their résumé. As a bonus, some of these courses are offered for free by universities such as Stanford and Johns Hopkins (through Coursera). Having any of these experiences on your data scientist’s résumé can help set you apart from your data-scientist-to-be peers.

Finding Your (Analytics) Passions

After entering the market, data scientists tend to take one of three directions in their career based on interests and passions. These three personas include business data scientists, efficiency data scientists, and machine learning (ML) data scientists.

Business data scientists are passionate about finding patterns in raw data and skilled at telling stories with that data to drive direction and insights for the business. One of the new terms in the market for a business data scientist is a “data translator.”

Efficiency data scientists pride themselves on making code as efficient as possible in terms of speed, computation, and modularization. These data scientists are often part of enterprise capability sharing teams and focus on enhancing internal company algorithms and processes.

The last group of data scientists, machine learning data scientists, are what people tend to think of when it comes to data science. They specialize in building propensity, forecast, optimization, and segmentation models to help drive business decisions.

All three types of data scientists are needed for a company to be successful so finding which area provides you the greatest job satisfaction is an important personal decision to make.

Diversifying Experience

Similar to finding your passion as a data scientist, finding new opportunities to diversify skill sets and experience is extremely helpful when trying to grow your career. There are many business sectors that require data science. Many of my retail coworkers have gone on to have great careers in media, finance, supply chain, social platforms, banking, and many other industries.

Having a diverse background can open many more opportunities in the future. Not only can having a robust background be more attractive to recruiters, it can also be helpful in case a market downturn occurs in a given business sector which may limit future career opportunities.

Although exploring different data science fields can be beneficial, as a developing data scientist you often have many opportunities to expand your skill set within your current company. Take retail, for example; data science expertise is required in sectors such as marketing, pricing, logistics, and merchandising. Being open to new positions provides the opportunity to gain new industry knowledge and become a more valuable and well-rounded employee. On top of the personal growth a new position can provide, it also helps build a network of supporters and resources to help advance your career.

Bridging Business Specializations

As data science has evolved over the years, so have the expectations for the role. The driving forces behind this evolution include companies investing more in data science teams, new tech stacks being introduced to the market, and a rapid influx of data that companies are now capturing.

Due to this rapid innovation, the lines between product management, engineering, and data science have become blurred. For example, with the emergence of cloud computing and the adoption of Python and PySpark, many projects are completed by both engineers and data science teams. Similarly, with data science involving more package building and automated dashboard creation throughout the market, product management and data science are also becoming more integrated. Due to the ever-growing interconnections across product, engineering, and data science, having experience in each can make a data scientist more valuable and efficient in their role. Your ability to communicate effectively with cross-collaboration stakeholders can be valuable in growing your career.

A Final Word

As this article demonstrates, there is no standard career path when it comes to data science, but hopefully these recommendations shine a spotlight on ways you can navigate and grow in your data science career.

About the Author

Chad Stripling is a director of data science at 84.51°, where he serves its consumer-packaged-goods clients by creating new tools and products to provide value and insights, creating new sciences and packages to enhance its product experience, and providing data science consultation utilizing its portfolio of product offerings. His current primary focus is 84.51° Collaborative Cloud. You can reach him via email or visit the 84.51° website.

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