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Data Scientist 2 - job post

MoEngage Inc
Bengaluru, Karnataka

Location

Bengaluru, Karnataka

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

  • 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 you'll 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 simple 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.
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