Before a company hires data science talent, they should understand the role and types of data scientists. Failing to differentiate between research, applied, and citizen data scientist can result in appointing the wrong people on crucial projects. To continue our previous episode's discussion, we invited Alex Vayner for a second time to get an answer to the question: What is a data scientist?

Alex Vayner is a Partner and Americas Data & AI Practice Leader for PA Consulting Group, an innovation and transformation consultancy. Alex has spent his entire career in data & analytics, with his last five roles focused on building and running high-performance data science teams and capabilities in consulting and corporate environments. Before joining PA Consulting, Alex ran the NA Data Science & AI practice at Capgemini. He joined Capgemini from Equifax, where he served as VP, Global Data Innovation Leader, building a team responsible for pioneering disruptive data & analytics solutions for clients across all industries.

Key Takeaways:

• There are three types of data scientists: Research data scientist, applied data scientist, and citizen data scientist.
• Research data scientists focus on discovering and applying methods to generate new algorithms.
• Applied data scientists take well-established models to solve business problems and configure them using open source libraries or tools like SAS.
• An applied data scientist wears many hats to manage machine learning engineering, ETL, and think about delivering business value.
• An industrialized product can be amplified under the supervision of citizen data scientists.
• It's better to have citizen data scientists deal with the business because they are less likely to use data science jargon.
• Citizen data scientists have more business knowledge than applied data scientists so they can update an industrialized model with new data.
• Machine learning engineers are focused on solving the production side of applications like computation, run-time, and parallelizing.
• To retain data scientists, you will have to balance the following three areas:

a) Interesting work: The problem can be solved by understanding how mature the organization is for data science projects and then deploying the right roles. Data scientists don’t just want to do ETL, data warehousing and visualization. They want to build models and do challenging work. Being honest with them about their role while hiring them will set expectations from the onset and make them less like to leave your company.

b) Culture: Millennials and Gen Z have trouble adjusting to traditional work cultures. They won't’ tolerate archaic HR policies like static annual assessments, keeping time of the work, working from nine to five and the idea of attendance. You will have to erode outdated practices to promote collaboration and trust.

c) Compensation: Compensations shouldn’t only be in terms of base pay or who offers the highest bracket. Getting creative by allocating budget for training and learning which allows data scientists to go to conferences of their choice is one of the ways you can compete with companies like Google and Amazon.