Top Jobs That Pave the Way for Becoming a Data Scientist

The demand for data scientists continues to grow as enterprises accelerate their dependence on data-centric technologies like AI, machine learning, and deep learning. The number of people with the required skills to fill these positions, however, is sadly lacking.

As a consultant in data science, as well as an educator and journalist, I’m frequently asked what lines of work can help pave the way for becoming a data scientist. In this article, I’ll encapsulate my thoughts about this important question in terms of what job roles are best able to lead to a career in data science.

[Related Article: How to Develop the Five Soft Skills That Will Make You a Great Analyst]

Top Jobs that Could Lead to Data Science

Let’s consider some specific job titles that could be considered a straight path toward a career in data science. All of these professions (and of course many others) possess the data skills necessary for a transition into data science. Here is the list in no particular order:

• Market researcher – someone who works with data to investigate the likes, wants, and behavior of consumers.
• Quantitative analyst – “quants” are people who work with data in the financial sector and forecast changes in the valuation of stocks, bonds, and other financial instruments.
• Economist – someone who works with data in terms of their influence in banking and other industries, but also in government and academia.
• Operations research analyst – someone who uses data along with mathematical methods such as simulation and optimization to get the most out of business process efficiency.
• Actuary – playing a central role in the insurance industry, actuaries use data and analysis to manage risk.
• Forensic accountant – someone who integrates skills in data, accounting, investigation, and analytics in support of criminal investigation and/or litigation.
• QA engineer – someone who uses statistical process control and other methods to improve business and production processes in manufacturing, and other industries.
• Meteorologist – someone who uses data, scientific methods, and mathematics to analyze and predict weather patterns and climate conditions.
• Epidemiologist – public health professional who uses data to investigate patterns and causes of disease and injury in humans.
• Nurse – these professionals do much more than you might think. Nurses are charged with the responsibility for healthcare quality data collection, analysis, and reporting in medical care facilities.

Innate Skills

The innate skills common to all of the above job roles can be characterized in the following way:

• Critical thinking – data scientists are critical thinkers, applying an objective analysis of facts to a given problem domain before formulating opinions or rendering judgments.
• Problem-solving – data scientists look at the world from many different perspectives. They look to understand the problem at hand in order to select the right tools from their toolbox. They work in a rigorous and complete manner in order to effectively explain the results of their problem solutions.
• Mathematics, statistics, probability theory – an effective data scientist excels at mathematics, statistics, and probability theory while having an ability to collaborate closely with enterprise thought leaders to communicate what is actually happening in the “black box” of complex algorithms in a manner that provides reassurance that the business can trust the outcomes and recommendations.
• Coding – data scientists understand how to write code and are comfortable handling a variety of programming tasks. These skills are not to be confused with the coding skills for a data engineer.
• Communication skills – a good data scientist must have the business savvy and curiosity to adequately interview the business stakeholders to understand the problem and identify relevant data sources. Further, a data scientist’s ability to communicate how an algorithm arrived at a prediction is a critical skill to gain a manager’s trust in predictive models being part of their business processes.
• Risk analysis – a strident data scientist understands the concepts of analyzing business risk, and making improvements in processes.

[Related Article: 5 Fields Hiring Data Scientists For 2019]

Conclusion

Arguably, there never has been a better time to consider a pivot in your career aspirations by taking advantage of the rising demand for data scientists. In fact, you can use the principles of data science to analyze the field using data. Consider the massive survey of more than 16,000 data scientists conducted by Kaggle to dive into questions around their education levels, undergraduate majors, job titles, salaries, and much more. Check out the analysis done by one data scientist using the Kaggle survey data set, along with Python code and a summary of findings including several data visualizations.