Senior Associate, Patient Analytics Role Overview We are hiring a Senior Associate who enjoys working hands-on with real-world data and turning it into answers pharma brand teams can act on. You will work on breast cancer engagements for US pharma clients, building the cohorts, algorithms and analyses for each client's requirements. Over time you will extend this work to other Oncology, Immunology and Rare disease areas. You will work closely with the engagement Manager and grow into leading workstreams yourself. Key Responsibilities: • Build breast cancer patient cohorts from claims and EMR data, defining patients by subtype (HR+/HER2-, HER2+, TNBC), stage (early vs. metastatic) and biomarker status. • Write and validate the logic behind core metrics: line-of-therapy assignment, regimen grouping, persistency, adherence, time on therapy and source of business. • Track biomarker testing patterns (HER2, ER/PR, PD-L1) and identify patients who are tested but not treated, or treated without testing. • Translate data outputs into clear charts and slides, and present findings in working sessions with client teams. • Build and maintain data pipelines and Tableau dashboards for ongoing brand and launch tracking. • Check your own work and analysts’ work for accuracy, and document code and methods so they can be reused across projects. • Support predictive modeling work such as patient finding and predictive segmentation, where needed. Qualifications: • Bachelor’s or Master’s degree in a quantitative field (e.g., Statistics, Data Science, Engineering, Life Sciences). • 3–5 years of experience in US pharma analytics, with most of it spent working directly with patient-level data. • Must have: Hands-on experience on breast cancer patient analytics project, building cohorts, LOT or patient journey analyses yourself. • Familiarity with oncology coding (ICD-10, NDC, J-codes) and experience with APLD/claims, EMR and lab datasets. • Strong SQL and Python/PySpark skills; experience building dashboards in Tableau or similar tools. • Good to have: Exposure to other Oncology, Immunology or Rare disease data, or to predictive / machine learning models. • Clear communicator, detail-oriented and curious about the clinical story behind the data.