Speakers

Isaac Slavitt
Co-founder and Principal Data Scientist at DrivenData

For Business

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Best Practices for Hiring Data Scientists

October 2023

Data science involves disparate skills from statistics to programming to communication. The difficulty of assessing this broad set of technical skills causes pain for both hirers and applicants. Candidates are often asked to answer arcane statistics questions on the spot, or live code solutions to the sort of data structure/algorithm CS problems that even professional programmers resent.

In this webinar, Isaac presents findings from a series of interviews with 20 data science hiring managers at leading organizations across industry (e.g. FAANG, finance, startups). He will discuss common patterns that emerged around both challenges and best practices, and make some actionable recommendations for data science teams looking to improve their hiring processes.

Key Takeaways:

Learn about the challenges and common mistakes when hiring data scientists.
Learn the best practices for how top companies hire data scientists.
Learn how to efficiently hire the best data science candidates.

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Summary

Recruiting data scientists has become a challenging and intimidating task for numerous organizations due to high demand and the complex skill sets required. Isaac Slavitt, co-founder of Driven Data, discusses the difficulties of hiring in this field, emphasizing that conventional methods frequently fail to accurately evaluate candidates' skills. He emphasizes the need for aligning the hiring process with actual job requirements and suggests using work samples, structured interviews, and practical coding tasks as effective strategies. Slavitt also highlights the importance of candidate experience, arguing that a respectful and engaging interview process can positively impact candidates' views of the company. He advocates for a more detailed approach to evaluating skills, avoiding trivia-like questions, and promoting a cooperative interview environment. As part of this, Slavitt urges organizations to consider the candidate's viewpoint, ensuring that the process reflects the actual work environment and culture, thereby attracting and retaining top talent.

Key Takeaways:

Work samples are the most predictive method for evaluating job compatibility in data science roles.
Structured interviews yield better results than unstructured ones in predicting candidate success.
Coding questions should focus on practical tasks rather than theoretical computer science problems.
The candidate's experience during the hiring process significantly impacts their decision to join the company.
Employ a detailed approach to evaluating skills, incorporating rich, layered questions.

Deep Dives

The Difficulty of Hiring Data Scientists

Hiring data scientists pre ...
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sents unique challenges due to the interdisciplinary nature of the role, which requires a mix of statistical, programming, and business skills. As Isaac Slavitt points out, this complexity makes it hard for conventional hiring methods to properly evaluate candidates. Organizations often receive a large number of applications, making it hard to identify the right fit. Furthermore, the repercussions of a poor hire in data science can be significant, affecting the accuracy of models and business decisions. Slavitt emphasizes the need for a hiring process that accurately reflects the skills needed for the role, rather than relying on outdated methods that focus on irrelevant skills or credentials.

Importance of Work Samples and Structured Interviews

Work samples and structured interviews emerge as the most effective tools in the hiring arsenal. According to Slavitt, work samples provide a realistic evaluation of a candidate's ability to perform job-related tasks, offering a tangible measure of their skills. Structured interviews, which are highly replicable and planned, also show high validity in predicting job performance. Slavitt recommends incorporating practical coding tasks and project discussions into the interview process, allowing candidates to demonstrate their problem-solving abilities and thought processes in a manner that mirrors actual job activities. This approach not only assists in evaluating candidates more accurately but also aligns the hiring process with the real-world demands of the role.

Coding Questions: Practicality Over Theory

Coding questions should be rooted in practicality rather than theoretical computer science problems. Slavitt notes that conventional questions focused on data structures and algorithms often fail to reflect the actual work data scientists perform. Instead, he suggests designing coding tasks that involve data exploration, visualization, and model building, which are more aligned with daily responsibilities. By doing so, organizations can better evaluate a candidate's practical skills and how they approach real-world problems. This shift in focus not only improves the evaluation process but also respects the candidate's time and expertise, providing a clearer picture of their potential fit within the team.

Improving Candidate Experience

Candidate experience is a critical, yet often neglected, aspect of the hiring process. Slavitt stresses that the interview process should simulate the actual working environment, offering candidates a glimpse into the company culture and team dynamics. A respectful and engaging interview process can significantly impact a candidate's decision to accept an offer. Slavitt advises against using the process simply as a test of knowledge, but rather as an opportunity to engage with candidates as potential colleagues. By focusing on cooperation and communication throughout the interview, companies can ensure a positive experience that reflects well on the organization and increases the likelihood of securing top talent.

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