https://ift.tt/XuFlA0Q The data science interview process can be intimidating, especially for newcomers to the field. According to our rec...
The data science interview process can be intimidating, especially for newcomers to the field. According to our recent poll conducted during a webinar, over 75% of aspiring data professionals feel unprepared or entirely new to the data science interview process. If you're among them, you're not alone—and this guide is for you.
To help demystify this key career step, Kishawna Peck, founder and CEO of Womxn in Data Science, recently shared her expert interview strategies during our Data Career Masterclass webinar. With 10+ years of experience leading data teams across nonprofits, fintech, and advertising, Kishawna brings valuable insights into what hiring managers truly seek in data candidates.
This guide distills her proven strategies to help you approach your next data science interview with confidence.
Understanding the Hiring Manager's Perspective
Before perfecting your interview answers, understanding what hiring managers look for provides valuable context for your preparation. As Kishawna explains, hiring managers evaluate candidates on three main dimensions:
- Technical and non-technical fit for the role
- Overall fit within the company culture
- Potential for growth and advancement
With these evaluation criteria in mind, you can tailor your preparation to address each area specifically.
1. Technical and Non-Technical Skills
To demonstrate your technical competence, Kishawna recommends:
- Researching the company's specific tools and technologies (via Glassdoor, company blogs, tech articles)
- Practicing coding problems and reviewing relevant frameworks
- Polishing your ability to explain data concepts clearly and concisely
"Sometimes you'll get the simplest questions around statistics, for example, in a data science interview because they're trying to make sure that you know the fundamentals," Kishawna notes. "Ensuring that you are ready on the technical side is very important."
2. Overall Fit for the Role
Beyond technical skills, hiring managers want to see that you align with their company culture and values:
- Research the company's mission and values (company website, recent news articles)
- Understand their market position (are they growing, contracting, pivoting?)
- Prepare stories that showcase your experience in relation to their values
3. Potential for Growth
Hiring managers want to visualize how you might develop within the organization:
- Highlight examples of past growth and adaptability
- Ask insightful questions about future projects
- Emphasize your commitment to continuous learning
As Kishawna advises:
If you meet 100% of the criteria in the job description, you shouldn't apply. You're overqualified. You want to have room for growth. From the hiring manager's point of view, I want to be able to visualize how you can grow within this role and grow within the company.
Mastering Different Types of Interview Questions
One of our webinar polls revealed that technical questions (45%) and case scenario-based questions (24%) cause the most anxiety for candidates. Let's break down the key question types and how to approach each effectively.
Technical and Case-Based Questions
For these questions, Kishawna recommends using the CODE method:
C - Clarify the question if needed
O - Outline your approach before diving in
D - Demonstrate your solution (write code or explain your process)
E - Evaluate your solution, discuss edge cases, and potential improvements
This structured approach shows your analytical thinking process and problem-solving skills.
Here's how this might look in practice:
Types of Technical Assessments
Kishana highlighted three common technical assessment formats:
- Take-home assessments: Completed on your own time, usually with a day or more to complete
- Pair programming: Working with a team member to solve a problem collaboratively
- Live coding: Solving technical problems in real-time during the interview
Kishawna advises:
A tip is to ask about the assessment format beforehand. This way you're not worried weeks in advance about how to prepare.
Behavioral Questions
While these questions might seem less intimidating, they're critical for evaluating your soft skills and past performance. The STAR method helps structure your responses effectively:
S - Situation: Set the scene
T - Task: Explain your role and responsibilities
A - Action: Describe what you did
R - Result: Share the outcome and its impact
For example:
Question: "Tell me about a time when you faced a difficult deadline. How did you handle it?"
Response: "At my previous company, I had a two-week deadline to develop a predictive model for our product team (Situation). I was responsible for delivering an accurate model that would help improve user engagement (Task). I focused on key features, automated the data cleaning process, and held daily check-ins with the product team to ensure alignment (Action). The result was that I met the deadline and the model's accuracy helped the team achieve a 15% increase in product engagement post-launch (Result)."
Culture Fit Questions
These questions help determine whether you'll thrive in the company's environment. Kishawna suggests using the ROW method:
R - Relevant experience
O - Opportunities for growth
W - Ways to contribute
For example:
Question: "How do you keep up with trends in data science, and what motivates you to stay current in such a rapidly changing field?"
Response: "I regularly engage with data science communities, take online courses, and attend conferences to stay up to date (Relevant experience). I'm particularly excited about advancing my skills in AI ethics, which aligns with your company's focus on responsible innovation (Opportunities for growth). I look forward to sharing insights and tools with the team to drive forward-thinking solutions and support collective growth (Ways to contribute)."
Asking Thoughtful Questions
One of the most overlooked aspects of interview preparation is developing meaningful questions to ask the interviewer. As Kishawna emphasizes, "When I have an interview and there are no questions from the interviewee, I get suspicious. Why don't you have any questions?"
Asking insightful questions serves two purposes: it demonstrates your genuine interest in the role and helps you evaluate whether the company is a good fit for your career goals.
Here are some powerful questions Kishawna recommends:
- "What advice would you give someone who wants to make an impact in this role within the first 90 days?"
- "How do you envision this role supporting the team and what are the key objectives for the next year?"
- "What do you most enjoy about leading this team, and how do you envision this role contributing to that environment?"
- "How does the data team collaborate with other departments?"
- "What role does the data team play in decision-making across the company?"
Kishawna's favorite closing question: "Do you have any hesitations about my profile and my ability to excel in this role?" This question provides an opportunity to address any concerns directly before leaving the interview.
"When you get a 'no' that they don't have any hesitations about your profile, that's actually really good and it means you probably got the role," she notes.
Addressing the Salary Question
Many candidates feel uncomfortable discussing compensation, but as Kishawna points out, "You're not volunteering—you're looking for a job to make money to take care of yourself and your family."
Here are her recommendations for handling salary discussions:
- Do your research: Use resources like Glassdoor to understand typical ranges for similar roles at comparable companies.
- Ask about the salary range early: Try to learn about the company's range during the initial screening call, before they ask about your expectations.
-
Know your numbers: Decide on your target and walkaway figures ahead of time.
Kishawna advises:
Usually for your target, so you would choose a range, and I would say whatever your bottom is, I would add 10 to 15% onto it. If your bottom was 50K and you know you would be super happy with that, I would say 55 to 70K.
- Consider negotiables beyond salary: If the offered salary is below your expectations, consider negotiating:
- Vacation time (often easiest to negotiate)
- Remote work flexibility
- Benefits start date
- Professional development budget
- Transportation costs
- Stand firm on your non-negotiables: Know what's truly important to you:
- Career growth opportunities
- Benefits package
- Work-life balance
- Team culture
Kishawna says:
You don't want to have resentment once you find out that other people negotiated and you decided that you didn't want to because you were trying to be nice. It's business. It's an exchange. So make sure that you negotiate.
Post-Interview Steps
After the interview concludes, Kishawna recommends these follow-up actions:
- Reflect on fit: Evaluate whether the role and team align with your values and career goals. "It is possible to go to an interview and realize that you do not want this role. It's not a good fit for you," Kishawna notes.
- Send a personalized thank-you email: Reference specific topics from your conversation. "Having like a basic canned one—we open it, but we're not serious about it. But if it is one that references things that we spoke about, then I usually read through it," Kishawna says.
- Follow up appropriately: Wait until after the timeline they provided before checking on your application status.
- Review the offer carefully: Read all terms thoroughly before accepting or negotiating.
Final Preparation Tips
As your interview approaches, Kishawna offers these final pieces of advice:
- Review your key skills and stories: Ensure they align with the role requirements
- Refine your questions: Prepare thoughtful questions that show your interest
- Get adequate rest: "It doesn't make sense to stress for an interview. You want to show up as your best. You want to be calm. You want to be confident."
- Plan something relaxing for afterward: Schedule something enjoyable after the interview to de-stress
Practice with AI Interview Simulation
To help you prepare more effectively, Kishawna has developed an enhanced LLM interview prompt that allows you to:
- Create personalized practice questions based on your resume and target job description
- Conduct mock interviews with voice or text
- Receive feedback on your use of the CODE, STAR, and ROW methods
You can access this valuable resource here: Enhanced LLM Interviewer Prompt
This interactive tool can help build your confidence, especially if you don't have someone available to conduct mock interviews with you.
Final Thoughts
The data science interview process may seem daunting, but with proper preparation and the right mindset, you can showcase your skills effectively and evaluate whether the role truly fits your career goals. Remember Kishawna's key advice:
You've gotten to the interview stage, so you must be doing something right. So stay calm and confident.
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