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10 good Lead Data Scientist interview questions
- Discuss the common pitfalls and risks in planning a data science project such as building a model that predicts whether a bank customer will default on their loan.
- What is the biggest team that you have ever managed and what challenges had you faced?
- Do you have experience in managing agile teams?
- A model your team has built performs 90% accuracy. What do you need to know in order to interpret whether this is good or not?
- Discuss a data-driven product that has really impressed you in recent years.
- How do you think one becomes a data scientist? What do you look for when you want someone to join your team?
- What is big data, really? Are you familiar with big data architectures?
- Off the top of your head, describe a product that uses data from Twitter to build something that people could conceivably pay money for.
- How do you stay current in your job and what are the challenges to doing this when you are a data scientist?
- How would you evaluate a feature such as Spotify’s Discover Weekly playlist?
Here are 10 essential interview questions and sample answers to help identify the best candidates for this role.
1. Discuss the common pitfalls and risks in planning a data science project such as building a model that predicts whether a bank customer will default on their loan.
Understanding the complexities and potential pitfalls of a data science project is essential. A candidate should be aware of issues like data bias, overfitting, and the importance of domain knowledge.
“One common pitfall is not having a diverse dataset, which can lead to biased predictions. It’s also crucial to ensure that the model doesn’t overfit the training data. Collaborating with domain experts can help in understanding the nuances of the data.”
2. What is the biggest team that you have ever managed and what challenges had you faced?
This question gauges the candidate’s leadership and management skills.
“I managed a team of 15 data scientists at XYZ Corp. The main challenge was coordinating between team members with varied expertise and ensuring effective communication. Regular check-ins and clear documentation helped address this.”
3. Do you have experience in managing agile teams?
Agile methodologies are becoming increasingly popular in data science projects.
“Yes, I’ve managed agile teams for several projects. The iterative approach of agile is beneficial for data science as it allows for flexibility and quick adjustments based on feedback.”
4. A model your team has built performs 90% accuracy. What do you need to know in order to interpret whether this is good or not?
Accuracy isn’t the only metric to evaluate a model’s performance.
“While 90% accuracy sounds impressive, I’d need to know the problem’s baseline accuracy, the precision, recall, and the F1 score. Additionally, understanding the business context is crucial. For some applications, even 99% might not be sufficient.”
5. Discuss a data-driven product that has really impressed you in recent years.
This question reveals the candidate’s industry awareness and what they value in data-driven products.
“I’ve been really impressed with the recommendation engine of Netflix. It’s not just about suggesting popular content, but how it tailors recommendations based on individual viewing habits.”
6. How do you think one becomes a data scientist? What do you look for when you want someone to join your team?
Understanding the candidate’s perspective on the data science journey and their hiring criteria is essential.
“Becoming a data scientist often involves a mix of formal education, self-learning, and practical experience. When hiring, I look for a strong foundation in statistics, programming skills, and most importantly, curiosity.”
7. What is big data, really? Are you familiar with big data architectures?
Big data is a buzzword, but its understanding is crucial for a lead role.
“Big data refers to datasets that are too large to be processed using traditional methods. It’s not just about volume but also variety and velocity. I’m familiar with architectures like Hadoop and Spark.”
8. Off the top of your head, describe a product that uses data from Twitter to build something that people could conceivably pay money for.
This tests the candidate’s creativity and ability to think on the spot.
“A product that analyzes trending topics and sentiment on Twitter to provide real-time market research for brands. This can help brands in their marketing strategies.”
9. How do you stay current in your job and what are the challenges to doing this when you are a data scientist?
The field of data science is always evolving, and staying updated is crucial.
“I regularly attend conferences, participate in online forums, and take courses. The challenge is the sheer volume of new information and discerning which trends are here to stay.”
10. How would you evaluate a feature such as Spotify’s Discover Weekly playlist?
Understanding how to evaluate real-world data products is essential.
“I’d look at metrics like user engagement, track skips, and feedback. Additionally, conducting A/B tests to compare with other recommendation methods can provide insights.”
What does a good Lead Data Scientist candidate look like?
A stellar Lead Data Scientist not only possesses strong technical skills but also demonstrates leadership, effective communication, and a deep understanding of the business context. They should be able to mentor junior team members, liaise with other departments, and drive data-driven decision-making at the highest levels.
Beware of candidates who focus solely on technical jargon without understanding the business implications. A lack of continuous learning or inability to explain complex concepts in simple terms can also be concerning.
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Lead Data Scientist Interview Questions
Managing a team of data scientists is a highly technical and demanding role that requires a candidate to be a jack-of-all-trades when it comes to developing data driven products and architectures. A typical team working on data science projects will encompass data scientists with a highly analytical capability as well as those whose role emphasizes a software engineering component dealing with production quality code. Finally, the team can include big data engineers, database specialists and roles with a strong research component such as machine learning engineers and natural language processing engineers. Thus at its core, the data scientist lead requires the efficient orchestration of a highly technical team and an in-depth understanding of the challenges of the different roles that comprise the team.
The ideal background for this candidate is an experienced data manager who has worked in a team and has both a strong theoretical background in fields such as machine learning and predictive modelling but also very strong software engineering skills. To be an effective lead, the ideal candidate will also have great communication skills, be well organized and able to prioritize and plan in a way that mitigates many of the risks that come with doing research and analyzing massive quantities of data. Finally, top candidates will also demonstrate a good understanding of data-driven services at the product level and how individual features impact the way customers interact and engage with a company’s product line.
A data science lead interview should include questions that could be asked for a general data scientist role. For examples of these, check out our interview questions for the data scientist (analysis) and data scientist (coding) roles.
Frequently asked questions
- What are the top Lead Data Scientist interview questions?
- Some top questions include discussing pitfalls in data projects, understanding of big data, and evaluating real-world data products.
- What is the best way to prepare for a Lead Data Scientist interview as an HR professional?
- Familiarize yourself with basic data science concepts, understand the specific needs of your company, and focus on leadership and communication skills.
- What are 3 qualities of a candidate for the Lead Data Scientist position?
- Strong technical expertise, leadership capabilities, and effective communication skills.
- How important is business acumen for a Lead Data Scientist?
- Extremely important. They should be able to translate data insights into actionable business strategies.
- What's the significance of soft skills for a Lead Data Scientist?
- Soft skills are crucial as they need to communicate complex data findings to non-technical stakeholders and lead diverse teams.