As a manager hiring and developing a small team of mid-level data scientists for an AI product, how do you balance delivering high-impact projects with fostering individual career growth and technical mentorship? Describe your approach to hiring, onboarding, goal-setting, and performance feedback.

I build a small, balanced team (typically 3–5 mid-level data scientists) by hiring for product instincts, ML fundamentals, and cross-functional collaboration. In onboarding I use a 30/60/90 plan with measurable ramp milestones—read the codebase, ship a small bugfix, own a model pipeline—cutting time-to-independent-contribution by ~40% in past roles. For goals I set 2–3 OKRs per quarter that pair product metrics (e.g., +12% feature adoption or -200ms latency) with personal growth objectives (promotion readiness in 12–18 months). I protect 20% of team time for mentorship, code reviews, and learning. Feedback is biweekly 1:1s and quarterly calibration reviews with concrete metrics and clear development plans. That structure lets us deliver high-impact features while each person advances technically and toward career milestones.