First 90 Days and Onboarding Plan Questions

In my first 90 days, success would mean I understand the business, the data, and the team’s priorities well enough to contribute reliably. I’d aim to learn the data pipelines, meet key stakeholders, and deliver a few well-scoped analyses or model improvements with minimal supervision.

By the first year:

• I would want to own projects end to end.
• I’d expect to contribute to at least one measurable business outcome, such as improved conversion, retention, forecasting accuracy, or operational efficiency.
• I’d also want to be trusted as a strong collaborator who communicates clearly with product and engineering.

By year three:

• I’d hope to be leading high-impact data science initiatives.
• I’d want deeper technical expertise in one area, plus enough breadth to influence strategy.
• I’d also like to mentor newer team members and help raise the team’s analytical bar.

For me, success is not just shipping models; it’s becoming someone the team trusts to solve ambiguous problems and drive better decisions over time.

Step 1 — Learn terminology: read domain primers, glossaries, and internal docs; shadow SMEs for a week. Step 2 — Identify metrics: compile existing KPIs, instrument key events, and validate definitions (DAU, conversion, false positive costs). Step 3 — Data assessment: inventory data sources, quality, and gaps; run exploratory data analysis to surface biases. Step 4 — Engage SMEs: schedule structured interviews, build hypotheses, and prioritize ambiguity-aware questions. Step 5 — Prioritize projects: use impact-effort matrix—quick wins to validate assumptions (e.g., a predictive signal with low engineering cost) and medium-term bets requiring data collection. Example: in healthcare, I first validated label quality with clinicians, then built a baseline risk model to drive a pilot before investing in complex models. This approach balances speed, rigor, and stakeholder trust.

First 30 days — Ramp & Discover

• Objectives: understand business priorities, data landscape, team processes.
• Activities: meet manager, product owners, engineers, analytics, and ETL team; review data catalog, pipelines, model registry, dashboards; run local environment, access sample datasets.
• Quick win: reproduce one existing KPI dashboard and a baseline model prediction for a key metric (e.g., churn score) end-to-end.
• Learning goals: get comfortable with company stack (data warehouse, ingestion, Python libs, model deployment tools, BI).
• Success metrics: access to required systems; reproduced dashboard + baseline model with documented assumptions; stakeholder sign-off on problem priorities.

Days 31–60 — Explore & Prototype

• Objectives: validate problem hypotheses, prototype high-impact models.
• Activities: perform EDA on top-priority dataset, define evaluation metrics, build 2 lightweight prototypes (e.g., improved churn classifier, demand-forecast baseline).
• Quick win: deliver a prototype that improves key metric (AUC lift or MAPE reduction) and a short slide + notebook for stakeholders.
• Learning goals: CI/CD for models, feature store, automated retraining pipelines.
• Success metrics: prototype shows measurable improvement over baseline (e.g., +5% AUC or -10% MAPE); peer code review completed; roadmap for productionization agreed.

Days 61–90 — Operationalize & Scale

• Objectives: productionize one project and embed with stakeholders.
• Activities: finalize feature engineering, implement monitoring, write tests, deploy to staging, knowledge transfer and runbook creation.
• Quick win: production-ready model endpoint or scheduled batch job with dashboard for business users.
• Learning goals: monitoring tools, governance, and compliance workflows.
• Success metrics: model deployed to staging or prod; automated pipeline and alerting in place; business KPI impacted and stakeholder adoption plan defined.

Ongoing: bi-weekly check-ins with manager, monthly presentations to stakeholders, and a 6-month roadmap for further value delivery.