Data Scientist 2

About the role

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.