Lead Data Scientist

Lead Data Scientist interview questions:

Managing a team of data scientists is a multifaceted and technically demanding role that necessitates a candidate to possess a wide range of skills when it comes to developing data-driven products and architectures. A typical data science team comprises individuals with varying expertise, including data scientists with strong analytical capabilities, those with a software engineering focus, big data engineers, database specialists, and roles emphasizing research, such as machine learning and natural language processing engineers. Consequently, a data scientist lead must effectively coordinate this diverse team and possess a profound understanding of the unique challenges associated with each team member’s role.

The ideal candidate for this role is an experienced data manager who has a track record of working in a team and possesses a solid theoretical foundation in fields like machine learning and predictive modeling, alongside robust software engineering skills. To be an effective lead, this candidate should also excel in communication, organization, and the ability to prioritize and plan to mitigate risks commonly associated with research and extensive data analysis. Top candidates will also demonstrate a comprehensive understanding of data-driven services at the product level and how individual features influence customer interactions and engagement with a company’s product line.

A data science lead interview should encompass questions that may be asked in a standard data scientist role. Additionally, it should emphasize leadership and management skills.

Role-specific questions:

• Discuss the common pitfalls and risks when planning a data science project, like developing a model for predicting loan default among bank customers.
• Can you share your experience in managing the largest team you’ve ever led, along with the challenges you encountered?
• Do you have prior experience in managing agile teams?
• If a model built by your team exhibits 90% accuracy, what additional information do you need to determine whether this level of accuracy is considered good or not?
• Reflect on a data-driven product that has left a lasting impression on you in recent years.
• How do you believe someone can become a data scientist, and what qualities do you seek when considering someone for your team?
• Define the concept of big data, and are you familiar with big data architectures?
• Off the cuff, describe a product that utilizes data from Twitter to create something that people might be willing to pay for.
• How do you stay updated in your role, and what challenges do you face when striving to do so as a data scientist?
• How would you assess a feature like Spotify’s Discover Weekly playlist?