Background and Entry Level Mindset Questions
Addresses a candidate's educational and early professional background together with an entry level learning orientation. Topics include relevant coursework, internships, projects, self-study, and clear articulation of current skill level and gaps. For entry level candidates, interviewers expect humility, eagerness for mentorship, and examples of quickly acquired skills. This canonical topic evaluates baseline experience plus readiness and attitude to grow from an early career stage.
HardTechnical
0 practiced
As someone early in your data science career, reflect on choosing a specialization (NLP, causal inference, ML infra) versus staying a generalist. Describe how you would decide using three factors: market demand, your personal strengths, and your team's needs. Walk through an example decision process and a first action for each possible choice.
EasyTechnical
0 practiced
Given a dataset with missing values and inconsistent timestamps, describe the immediate first three steps you would take to assess its quality and begin cleaning the data before any modeling work. Be explicit about checks, tools, and quick visualizations you might use.
HardTechnical
0 practiced
Design a personal 12-month development roadmap to progress from entry-level to mid-level data scientist. Include technical skills, soft skills, sample projects, mentorship activities, and measurable success criteria (what outcomes indicate promotion readiness).
MediumBehavioral
0 practiced
Describe a time you worked with ambiguous or incomplete problem requirements. How did you frame the problem, what assumptions did you state explicitly, what minimal experiments or prototypes did you run to validate assumptions, and how did you communicate progress to stakeholders?
MediumTechnical
0 practiced
How would you assess whether a dataset collected during an internship is biased with respect to a demographic attribute? Describe specific checks (distributions, label imbalance, performance by subgroup), statistical tests, and corrective measures you might consider.
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