Back
Mid-level, popular Data Scientist title, Bangalore location, and known startup brand raise competition.
Product-centric modeling, experimentation, and KPI ownership require some domain-specific experience, so moderately sensitive.
No explicit years but production ML, causal inference, and deployment requirements imply moderate filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own a scoped data science problem: hypothesis formulation, success metrics, evaluation plans, and calibrated predictive or causal model delivery.
Develop and deploy repeatable production pipelines integrated with product workflows, ensuring ongoing model performance monitoring and iteration post-launch based on telemetry.
Collaborate closely with PMs and engineering teams to align on KPIs, launch model-driven features, and improve team processes beyond individual tasks.
Experience Required: Not explicitly mentioned in the JD.
Must be capable of building calibrated predictive or causal models with sound probability and effect estimates.
Ability to package work into repeatable pipelines and integrate them into production workflows with basic model monitoring.
No explicit degree, notice period, or location requirements mentioned.
Operates independently on scoped feature problems with minimal guidance, focusing on end-to-end model delivery and impact.
Strong in applied ML/AI techniques, experimentation design (A/B testing, significance measures), and framing DS problems tied to product KPIs.
Experienced in cross-functional coordination with Product Management, Product Engineering, and ML/AI Engineering to ship impactful production ML features.