Data Scientist, Identity team

Ready to shape the future of ride-hailing services with data-driven decisions? At Bolt, we’re looking for a Mid Data Scientist to join Bolt’s Identity group. You’ll use data to uncover opportunities, frame the right questions, and shape how we verify users, enable communications, authenticate accounts, and build privacy-respecting insights.

Location: Warsaw, Poland | Bucharest, RomaniaView all roles
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About the role

Identity sits at the core of Bolt’s platform and powers safe, seamless user experiences. You’ll lead discovery analyses across Identity flows (Verification, Comms, Authentication, User Insights & Privacy), translate patterns into hypotheses, and partner with product and engineering to design experiments and data-driven changes. Expect to prototype lightweight models where they’re the simplest, highest-leverage tool, keeping the emphasis on problem-finding, evidence, and impact.

What you can expect
Main tasks and responsibilities:
Probe Identity funnels to find friction and opportunity (e.g., verification drop-offs, auth success/recovery, comms deliverability), size the impact, and turn insights into prioritized, testable ideas.
Design and analyze A/B tests, and deliver crisp readouts with recommended decisions.
Prototype lightweight models and decision rules (e.g., verification-related, anomaly/spoof detection, duplicate-account signals) with careful offline/online evaluation and simple monitoring; collaborate with engineers for productionization.
Discover and evaluate new privacy-respecting features (document, behavioral, device/network history) that improve identity quality and user safety; articulate trade-offs and expected impact clearly to stakeholders.
What we expect
About you:
1–4 years in a data-science or quantitative product role, with strong statistical thinking and curiosity for problem discovery.
Proficient in SQL and Python (Pandas/NumPy; scikit-learn for light modeling); experienced in working with large datasets and communicating trade-offs clearly.
Hands-on with experimentation and with causal/observational reasoning when randomized tests aren’t feasible.
Comfortable turning ambiguous spaces into measurable metrics, hypotheses, and shipped improvements; you default to the simplest approach that moves the needle.
Nice to have: exposure to computer vision or biometrics (images/selfies, MRZ/OCR, face matching, liveness), familiarity with evaluation practices.
Experience is great, but what we really look for is drive, intelligence, and integrity. So even if you don’t tick every box, please consider applying!

Apply now and join us to make cities for people, not cars.

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