Data Scientist 2
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At a glance
Job overview
MoEngage is hiring a Data Scientist 2. The Data Scientist 2 (DS-2) role at MoEngage involves deep problem ownership and iteration with product managers and engineering. A DS-2 transforms a scoped product problem into a calibrated model or decision system, ships it through the standard production path, and owns its post-launch performance. This role operates with minimal guidance on defined features, focusing on improving team processes and delivering product outcomes.
Key focus areas include 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, and Stress-test findings with simple mental models or simulations before trusting them.
Important skills include Data-Driven Decision Making, Predictive Modeling, Causal Models, Uncertainty Impact Analysis, Technical Expertise, and Reproducible Environments.
Skills & qualifications
Skills
Full job description
Data Scientist - 2 (DS-2)
Job family: Data Science Level: DS-2 (equivalent to MLE-2 / AIE-2) Scope of impact: Feature 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
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A scoped modelling problem framed as a DS task: hypothesis, success metric, offline and online evaluation plan.
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Calibrated predictive or causal models with well-behaved probabilities and effect estimates.
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Repeatable pipelines integrated with production workflows, not one-off notebooks.
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Basic model monitoring for the features you ship.
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Post-launch performance of your model and its link to the target KPI; iterate using telemetry.
What you do not own (yet)
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Platform uptime and shared serving infrastructure (ML Engineering owns this).
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Domain-wide priority setting across multiple initiatives (DS-3 and above).
What you'll do (proficiency expectations at L2)
Data-driven decision making
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Build calibrated predictive or causal models with sound probability and effect estimates.
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Articulate the impact of uncertainty and select an applicable course of action with minimal guidance.
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Stress-test findings with simple mental models or simulations before trusting them.
Technical expertise
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Set up fully reproducible environments for your own work and share the guides with peers.
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Package work into repeatable pipelines and integrate them with production workflows.
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Implement basic model monitoring.
Applied ML/AI/DS
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Frame and scope an opportunity as a DS problem, and pick the right solution family (prediction, optimization, causal).
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Review recent literature, build reproducible pipelines, and fairly compare alternative models.
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Run controlled pilots that connect model uplift to a target KPI.
Experimentation and inference
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Frame a testable hypothesis and pick the right design (A/B or hold-out).
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Run multi-metric or stratified tests with power checks and CUPED variance reduction.
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Conclude using confidence intervals, state the limitations, and tie results back to a target KPI.
Strategy and influence
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Scope an opportunity into a well-posed DS problem, naming the RoI and the product and process changes it implies.
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Align stakeholders on the KPI leverage of a proposed approach and secure agreement on scope and goals.
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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
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PM: co-own the outcome and prioritisation for your feature.
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Product Engineering: integrate your model into customer-facing experiences.
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ML Engineering / AI Engineering: consume platform primitives; collaborate on evaluation, reliability gates, and production readiness.
What we expect from a strong DS-2
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Ships production artifacts on the standard path, not prototypes that stall at the production boundary.
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Improves at least one team process (templates, reviews, reproducibility) beyond their own tasks.
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Owns outcome integrity: model outcomes stay aligned with product outcomes after launch.
You've read the whole posting — now see how you match it.