Senior Data Analyst, Pharmaceutical Industry

Job description

**Job Description: Senior Data Analyst, Pharmaceutical Industry** **Job Details**: 90%remote position in the United States, FT Perm W2 employee **Travel Requirements:**up to 10% domestic travel each year forcompany events and socials **Time Zone Preference:**USEast Coast **Role Purpose:**  The Senior Data Analyst supports pharmaceutical brand, commercial (marketing), and analytics teams by playing a critical technical role connecting healthcare data to media to drive clear, actionable insights. This role requires strong technical analytics skills, rigorous statistical thinking, and the ability to communicate findings in a simple, compelling way to both technical and non-technical stakeholders within the company. The ideal candidate is articulate, collaborative, detail-oriented, and comfortable working with imperfect data and complex data structures to enable advanced analysis, including AI and machine learning applications.  **Core Responsibilities:**  - Perform **extensive SQL-based data transformations**, integrating data from multiple internal and external sources (e.g., claims, media, CRM, EHR-derived datasets). - **Define, validate, and govern metrics** aligned to pharmaceutical business questions (e.g., brand performance, HCP engagement, patient journeys). - Apply **statistical reasoning** to analyze trends, identify drivers of performance, and ensure analytical rigor in insights and recommendations. - **Support AI data modeling and machine learning initiatives** by preparing, validating, and structuring scalable datasets in collaboration with data science and technology teams. - **Maintain and iterate on analytical datasets and workflows** as new data is ingested. - **Conduct** **thorough exploratory data analysis (EDA)** to uncover patterns, anomalies, and opportunities within complex healthcare datasets. - **Interpret and translate analytical findings into clear business insights**, tailored for stakeholders across marketing, analytics, strategy, and leadership teams. - **Manually scrub, structure, and normalize data** as needed to ensure data quality and analytical readiness, particularly for deep-dive analyses. - Prepare datasets to support **advanced analytics, including exploratory and explanatory machine learning techniques,** ensuring accuracy, consistency, and scalability. - Develop **data visualizations and dashboards** that clearly communicate insights and support decision-making. - **Collaborate cross-functionally** with data science, technology, media, strategy, and client-facing teams to align analyses with business needs. - **Document**methodologies, assumptions, and data limitations to support transparency and reproducibility. **Cross-functional Interfaces:**  - Strategic Solutions - Data  & Technology **Success Indicators (What good looks like):**  - Bachelor’s degree or equivalent work experience - Experience working with cloud data warehouses (**e.g., Big Query).** - Demonstrated hands-on experience **developing, testing, and refining AI models and machine learning solutions** to address business and analytical needs. - Strong proficiency in **SQL**, including complex joins, transformations, and performance optimization. - Solid foundation in **statistics and analytical reasoning** (e.g., trend analysis, hypothesis testing, segmentation). - Demonstrated experience with **exploratory data analysis** and working with large, complex datasets. - Ability to **simplify complex data and insights** for diverse audiences. - Experience with **data visualization tools** (e.g., Tableau, Power BI, Looker, or similar). - Proven ability to work **collaboratively** in cross-functional teams. **Preferred Qualifications**  - Experience working with pharmaceutical commercial or media data (e.g., claims, prescription, media, CRM, hub data). - Familiarity with **AI/ML concepts** and data preparation requirements for modeling. - Experience using **Python or R for analytics** and data manipulation. - Understanding of **pharma**compliance, privacy, and data governance considerations. - **Experience working** in a **remote setting**, collaborating cross-functionally with individuals across teams and in different time zones. **Key Competencies**