Theme: Grows and Acts completes scoped modelling tasks and improves team process
Why this role exists
Product outcomes need deep problem ownership and tight iteration with PMs and product engineering. A DS-2 turns a scoped product problem into a calibrated model or decision system, ships it through the standard production path, and owns its performance after launch. You operate with minimal guidance on a defined feature, not the whole domain.
What you own
- A scoped modelling problem framed as a DS task: hypothesis, success metric, offline and online evaluation plan.
- Calibrated predictive or causal models with well-behaved probabilities and effect estimates.
- Repeatable pipelines integrated with production workflows, not one-off notebooks.
- Basic model monitoring for the features you ship.
- Post-launch performance of your model and its link to the target KPI; iterate using telemetry.
What you do not own (yet)
- Platform uptime and shared serving infrastructure (ML Engineering owns this).
- Domain-wide priority setting across multiple initiatives (DS-3 and above).
What youll do (proficiency expectations at L2) Data-driven decision making
- Build calibrated predictive or causal models with sound probability and effect estimates.
- Articulate the impact of uncertainty and select an applicable course of action with minimal guidance.
- Stress-test findings with straightforward mental models or simulations before trusting them.
Technical expertise
- Set up fully reproducible environments for your own work and share the guides with peers.
- Package work into repeatable pipelines and integrate them with production workflows.
- Implement basic model monitoring.
Applied ML/AI/DS
- Frame and scope an opportunity as a DS problem, and pick the right solution family (prediction, optimization, causal).
- Review recent literature, build reproducible pipelines, and fairly compare alternative models.
- Run controlled pilots that connect model uplift to a target KPI.
Experimentation and inference
- Frame a testable hypothesis and pick the right design (A/B or hold-out).
- Run multi-metric or stratified tests with power checks and CUPED variance reduction.
- Conclude using confidence intervals, state the limitations, and tie results back to a target KPI.
Strategy and influence
- Scope an opportunity into a well-posed DS problem, naming the RoI and the product and process changes it implies.
- Align stakeholders on the KPI leverage of a proposed approach and secure agreement on scope and goals.
- Coordinate with engineering and product leads to launch features where the model provides core value; shape planning and risk assessment.
How you work with others
- PM: co-own the outcome and prioritisation for your feature.
- Product Engineering: integrate your model into customer-facing experiences.
- ML Engineering / AI Engineering: consume platform primitives;
collaborate on evaluation, reliability gates, and production readiness.
What we expect from a strong DS-2
- Ships production artifacts on the standard path, not prototypes that stall at the production boundary.
- Improves at least one team process (templates, reviews, reproducibility) beyond their own tasks.
- Owns outcome integrity: model outcomes stay aligned with product outcomes after launch.
📌 Data Scientist 2 (Bengaluru)
🏢 MoEngage
📍 Bengaluru
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