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TDWI Upside - Where Data Means Business

Finding Talent on the Periphery

Now is a great time to find data science talent by expanding your search from the mainstream to the periphery.

In a world where data and analytics have become ever more important, there are two trends that hiring managers need to clearly understand. The first is that demand for data scientists continues to be high. According to the firm BurtchWorks, 81% of data science and analytics teams plan on hiring in the latter part of 2021. Although colleges and universities are producing more data science graduates, the market for these skills is still tight.

For Further Reading:

Breaking Through Barriers in Traditional Hiring and Recruiting: 3 Stories

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Building Your Big Data Analytics Staff

Second, as the world is emerging from the pandemic, employees are reevaluating their careers and their current employment. As they do, many employees are resigning from their positions in search of greener pastures.

As this Great Resignation commences, it is in the best interests of hiring managers to start looking at resources on the periphery of the data science space to find those with the potential to be tomorrow's great data scientists. Because data science is the intersection of business/domain knowledge, technology, and the application of the scientific process to problems, where better to look for these resources than in adjacent disciplines that are engaged in one or more of these domains of knowledge?

Looking at the U.S. Bureau of Labor Statistics, there are several groups of employees that can be shaped into great future data scientists.

Business and Financial Occupations

This is a group of resources that are heavily focused on business data and have critical domain expertise in the areas where data science teams are focused.

  • Accountants and auditors: Accountants spend their day deep in financial data. They focus on financial opportunities and risks, looking for patterns in the data. They have a keen eye for tying disparate data sources together to validate the numbers. Most of these individuals are highly skilled in spreadsheets and many have started to improve their analysis by leveraging tools such as R or Python to expand their capabilities.

  • Compensation, benefits, and job analysis specialists: Compensation and benefits analysts leverage data to compare compensation and benefits plans. Their target is to evaluate whether their companies are effective in balancing efforts to care for employees while at the same time optimizing corporate costs. Their analysis combines financial analytics with psychographic analytics. Their analysis of people gives them the ability to see patterns in human behavior.

  • Cost estimators: Cost estimators are highly engaged in working with predictive data. They collect and analyze data to estimate the time, money, materials, and labor required to accomplish corporate goals. They are comfortable with using patterns from historical data to create models that estimate future results.

  • Financial analysts: Financial analysts are similar to accountants and auditors as far as their experience with financial data. Financial analysts are often focused on problems associated with optimization. They search for ways to increase corporate profitability by organizing company resources to perform optimally.

  • Logisticians: Logisticians are similar to their counterparts in the finance department but are focused on the organization's supply chain. They are looking at the data associated with the entire product life cycle, identifying ways to optimize the process. They are often focused on problems associated with minimizing costs while still expediently delivering high-quality products.

Computer and Information Technology Occupations

For Further Reading:

Breaking Through Barriers in Traditional Hiring and Recruiting: 3 Stories

Career Advice: 5 Musts for Breaking into Analytics

Building Your Big Data Analytics Staff

These resources have the technical expertise associated with launching data science projects into production. Some of these workers might already be on data science teams as the technology resources and, with additional training or experience, could become great data scientists.

  • Computer programmers: Computer programmers write and test code that allows computer applications and software programs to function properly. They are experts at breaking down business problems and turning them into units of code that can transform data inputs into data outputs. They also have experience and knowledge in testing the results to ensure that the transformation process is functioning correctly.

  • Software developers, quality assurance analysts, and testers: Software developers are responsible for analyzing user requirements and developing software applications that meet those needs. Software developers are often part of a product team composed of testers, quality assurance analysts, and business analysts, all working together to deploy a working unit into production.

  • Computer systems analysts: Computer systems analysts are sometimes called systems architects. They are involved in looking at the full stack of technology needed to support computer systems. They have expertise in designing systems that can work in production in a manner that is reliable, secure, and scalable. They have the maturity to understand how the solutions to problems need to be able to work in perpetuity and not just as a one-and-done product.

  • Information security analysts: Information security analysts have the challenging task of looking at network or application data and identifying anomalous patterns. They are constantly looking for user behaviors or system behaviors that are outside the norm. When they identify these, they work to establish boundaries that prevent them from happening in the future.

Life, Physical, and Social Science Occupations

These resources are not often engaged with data science teams, but they are experts in the scientific method. This maturity of the process can bring new insight and understanding to a data science team.

  • Biochemists and biophysicists: Biochemists and biophysicists study the chemical and physical principles of living things and biological processes. They work in areas of data collection and pattern analysis to identify how the processes associated with living things function. They then convert this hypothetical knowledge into real-world applications. They don't stop at the data level or the pattern level. They push that into the application stage of the process.

  • Chemists and materials scientists: Chemists are focused on the atomic or molecular level of material. They are experts in looking at the smallest units of data and identifying patterns in these interactions. They understand how to leverage this atomic data to establish laws and principles that have a higher level of importance. They can decompose visible observations to their atomic components and find the models that explain what is happening.

  • Economists: Economists are similar to logisticians in their effort to look at the production of goods and services. Instead of looking at them at an organizational level, economists look at it at the macro level. They collect and analyze data, research trends, and evaluate issues to do with the global, national, or regional economy. They are experts at finding society-level patterns within the data. They usually have more advanced knowledge of mathematical models and statistical techniques, and are often comfortable with leveraging these models to perform advanced forecasting and estimation.

  • Geoscientists: Geoscientists leverage geographic and geologic data to understand the earth's resources. They have experience using this data to create maps that can guide others. They can use these overview models to extract knowledge of where critical resources are or may be found.

  • Microbiologists: Microbiologists work to understand how organisms live, grow, and interact with their environment. Their understanding of evolutionary biology and how the patterns found in nature can be duplicated in computer code to model organizational behavior that can open new understanding to drive a company forward.

As you are looking for the next resources for your data science team, resist the urge to be myopic. Look at the periphery to identify resources in other job categories who might be poised to bring great wisdom and knowledge to your data science team. As we witness a large-scale migration of labor resources between companies, industries, and job paths, now is the time to find those resources who have been hidden in the past but have the potential to become your future data science superstars.

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