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 chance 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 (India)
🏢 Moengage
📍 India