Senior Data Scientist (Product Analytics) — Session Analyzer $$$$

Role summary
We are looking for a Senior Data Scientist to build the core intelligence layer of our Session Analyzer product: scoring, audience segmentation, and prescriptive recommendations for pharmaceutical marketing teams. You will operate as a “full-stack” data scientist across data validation, data engineering (prod pipelines), ML modeling, causal measurement, and AI-assisted workflows (agents for insights and data diagnostics). You will work directly with the Product Lead and Tech Lead.
 

What you will do

• Design and ship scoring, segmentation, and recommendation features based on pixel session/event data, NPI-enriched profiles, and 3rd-party datasets.
• Build a data quality & validation framework from scratch: tests, monitors, anomaly detection, drift tracking, and root-cause workflows across the data chain.
• Deliver batch PoC (days latency) and evolve to near-real-time MVP (hours latency) pipelines and feature tables.
• Develop and evaluate ML models for engagement prediction and audience targeting; ensure calibration, stability, and business-relevant lift.
• Define and implement measurement/attribution approaches to support ROI uplift and explain “what worked / why”.
• Build AI agents for (1) automated data issue diagnostics and (2) insight/presentation generation, with guardrails and traceability.
• Produce outputs as score tables, top insights, aggregates/features, dashboards/reports, and APIs for audience activation and CDP/CRM integrations.

Success metrics

• Improved Engagement score
• Measurable uplift in campaign ROI
• Reduced time-to-insight
• Increased share of HCPs with actionable recommendations
• Improved coverage/quality signals (e.g., NPI resolve rate progress, event capture reliability)

Requirements (must-have)

• 5+ years in Data Science / Analytics roles with production ownership.
• Strong Python + SQL; hands-on building production pipelines and data products.
• Experience with event/session analytics, aggregation modeling, and working with noisy web/pixel data.
• Proven ability to build propensity/engagement models, segmentation, and recommendation logic; strong evaluation practices.
• Working knowledge of causal inference / attribution methods for marketing effectiveness.
• Experience with GCP / BigQuery in production; familiarity with ClickHouse/Postgres is a plus.
• Ability to operate end-to-end: problem framing → data validation → modeling → deployment → monitoring → stakeholder comms.
• Hands-on experience building and operating data-driven marketing/advertising analytics products, including event-level measurement, audience segmentation, activation workflows, and performance attribution (AdTech/MarTech environments).

Nice-to-have

• Experience with agents (LLM-based workflows), automated insights, and safe deployment patterns.
• Familiarity with data quality tooling (e.g., Great Expectations/dbt tests) and observability.

Soft skills & values (required)
Openness

• Direct feedback, acknowledges mistakes, listens and acts on critique.

Internal Locus

• Takes responsibility for outcomes, analyzes own decisions first, learns quickly from failures.

Get it done

• Delivers end-to-end, removes blockers independently, completes work to production-grade finish.

Self-improvement

• Continuously adopts new tools (incl. AI), improves workflows, increases productivity without heavy training overhead.

Ownership

• Treats the product and system results as their responsibility, not just personal tasks/KPIs; minimizes need for micromanagement.