What Companies Look for When Hiring Data Scientists

Hiring in data science has become very competitive for both companies and professionals in recent years. Fueled by the Great Reshuffle, data science positions are in high demand which can greatly benefit new and seasoned data science professionals alike. But in order to get hired in such a competitive landscape, candidates need to be prepared to exhibit the skills and knowledge that are currently in demand. A good place to start is looking at the industry as a whole and what the current trends and needs are for companies utilizing data science.

What Companies Look for When Hiring Data Scientists

March 8, 2022


Hiring in data science has become very competitive for both companies and professionals in recent years. Fueled by the Great Reshuffle, data science positions are in high demand which can greatly benefit new and seasoned data science professionals alike. But in order to get hired in such a competitive landscape, candidates need to be prepared to exhibit the skills and knowledge that are currently in demand. A good place to start is looking at the industry as a whole and what the current trends and needs are for companies utilizing data science.

Today’s businesses tend to have basic operational and KPI reporting but often lack deeper analytical capabilities. These capabilities are crucial because they help increase visibility into core processes and provide insights that help the organization make better decisions. Some of the factors that greatly increase analytical capabilities, and subsequently data driven decision making, are robust data stores, machine learning, optimization and automation, and data literacy and communication.

 

Today’s businesses tend to have basic operational and KPI reporting but often lack deeper analytical capabilities.

 

There is more data in the world than ever before and in order to best utilize all of that information, companies need to organize and store their data in a way that facilitates analytics. An ideal example of this would combine data from multiple sources across an organization as well as external data, such as demographics and industry metrics, which can provide added context. To accomplish this, companies need talented data engineers that can build these structured and accessible data stores as well as data scientists who can make use of such an infrastructure to perform exploratory data analyses in order to identify and uncover insights.

 

Data scientists who can accurately predict trends in metrics such as engagement, demand, conversions, revenue, cost, and churn can help expedite decision-making at scale based on how those decisions have been made historically.

 

Alongside performing robust analyses on existing data to identify insights, companies are also looking to generate more accurate predictions and forecasts by utilizing machine learning. Data scientists who can accurately predict trends in metrics such as engagement, demand, conversions, revenue, cost, and churn can help expedite decision-making at scale based on how those decisions have been made historically. This type of business optimization is very valuable but it’s only one aspect of a much larger market trend. Optimization in the form of automation is another area that companies are looking to improve upon. Candidates who can identify, and subsequently automate, time-consuming, information-intensive processes that were previously done manually are invaluable to businesses that want to streamline their administrative functions.

 

Not only do data scientists need to analyze and produce insights the business can act on, but they need to communicate those insights effectively to team members and stakeholders that may not have an analytical background.

 

The final hurdle for making use of large amounts of data, analytics, machine learning models, and process optimization is stakeholder buy-in. This requires data literacy across the entire organization. Not only do data scientists need to analyze and produce insights the business can act on, but they need to communicate those insights effectively to team members and stakeholders that may not have an analytical background. This can be done at an organizational level through democratizing data and utilizing data scientists and engineers as ambassadors for data literacy. By encouraging data scientists to teach their business minded team members how to find the data they need, interpret the insights from it, and use those insights to answer questions and make informed decisions, you can develop a data-driven culture at an organization. Additionally, candidates who can incorporate effective data visualization and storytelling practices into their work can make all the difference in communicating insights to stakeholders.

Data driven organizations are always looking for talented candidates but industry needs change over time. In order for candidates to be desirable in a competitive landscape, they need to understand such trends and be prepared to showcase their skills and knowledge accordingly. This is true for large organizations with robust data science teams as well as data science consulting firms such as Fulcrum Analytics. You can stay up to date on the latest industry trends and data science practices throughout your job search and into your next position by following us on Linkedin.

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