Data science teams are an integral part of early-stage start-ups, growth-stage start-ups and enterprise companies. A data science team can include a wide range of roles that take care of the end-to-end machine learning lifecycle from project conceptualization to execution, delivery, and monitoring. The manager of a data science team in an enterprise organization has multiple responsibilities, including the following:
- Hiring of a data science team
- Cross-functional stakeholder management
- Career development and mentorship
- Performance appraisals
Ownership of the entire data science program, including delivering ROI on investments
As the data science manager, it’s critical to have a structured, efficient hiring process, especially in a highly competitive job market where the demand outstrips the supply talent. A transparent, thoughtful, and open hiring process sends a strong signal to prospective candidates about the intent and culture of both the data science team and the company, and can make your company a stronger choice when the candidates are selecting an offer.
In this guide, you’ll learn about key aspects of the process of hiring a top-class data science team. You’ll dive into the process of recruitment, interviewing, and evaluating candidates to learn how to find the ones who can help your business improve its data science capabilities.
- Benefits of an Efficient Hiring Process
- Building a Funnel for Talent
- Defining Roles and Responsibilities
- Interviewing Candidate for Data Science Teams
- Evaluating Candidate Performance
- Extending an Offer
Benefits of an Efficient Hiring Process
Recent events have accelerated organizations’ focus on digital and AI transformation, resulting in a very tight labor market when you’re looking for data science skills like machine learning, statistics, and programming.
A structured, efficient hiring process enables teams to move faster, make better decisions, and ensure a good experience for the candidates. Even if candidates don’t get an offer, a positive experience interacting with the data science and the recruitment teams makes them more likely to share good feedback on platforms like Glassdoor, which might encourage others to interview at the company.
How to Build the Best Data Science Team
Before you dive deep into our guide on hiring data scientists for your team, we have curated speakers ranging from Chief Data & Analytics Officers to global executives discussing everything you need to know before getting started with hiring.
Winning the Data Science Talent Race
The market for data science professionals continues to be red hot. The switch to remote work has both opened up the candidate pool and made the market more competitive as large organizations are reaching into new markets for candidates. It also means your existing team is getting multiple offiers. Learn what approaches are working to attract and retain talent and trends to be aware of.
How to Build the Best Data Science Teams
In our fireside chat John Thompson, best-selling author and global head of advanced analytics and AI at CSL Behring, and our chief customer officer Dave Cole discussed the trials and tribulations they've encountered over their combined 50+ years in the field of analytics and data science. We break down their ideas from the chat below.
In this guide, you’ve looked at an overview of the process of hiring a data science team, including the roles and skills you might be hiring for, the interview process, and how to evaluate and make decisions about candidates. In a highly competitive data science job market, having a robust pipeline of talent, and a fast, fair, and structured hiring process can give companies a competitive edge.
Once a data science team is in place, it needs to take machine learning models to production to create business impact. MLOps is a core function of data science teams, and it can help your company accelerate their model velocity and unleash data science at scale. The Domino Enterprise MLOps platform provides the necessary infrastructure, tools, and materials for enhanced coordination within data science teams to fast-track the path to production.
Have your data science team built out? Learn more about managing your data science team.