Senior Manager, Oncology Data Science, Hematology
The Senior Manager of Data Science & Analytics supporting hematology is responsible for developing data-driven insights to inform commercial strategy, brand planning, and operational effectiveness. This role requires hands-on data analysis and close collaboration with cross-functional teams to ensure data is leveraged to support business-critical decisions for Adcetris and Elrexfio. The Senior Manager will manage various data assets, including claims, sales, and specialty pharmacy data, and will work across oncology commercial analytics and commercial teams to integrate insights into the franchise’s strategic direction. This position will report to the Senior Director of Data Science & Analytics within Pfizer’s Oncology Commercial Analytics team.
Key Responsibilities: Data Analysis and Insights Generation- Advanced Analytics: Develop and implement advanced analytics models, using techniques such as machine learning, predictive modeling, and statistical analytics, to drive insights into customer behavior, market trends, and product performance.
- Secondary Data Analysis: Leverage multiple external data sources (e.g., claims data, specialty pharmacy data, non-retail sales) to generate actionable insights that address strategic business questions.
- Data Asset Management: Partner with Commercial Information Management and data governance teams to ensure the proper stewardship of data, including the integration of new data assets to enhance decision-making.
- Data Quality and Consistency: Ensure the quality, accuracy, and timeliness of data used for analysis by collaborating with IT, Marketing, and other cross-functional teams to maintain clean and reliable datasets.
- Data Sourcing: Collaborate with the Director of Oncology Data Enablement to identify and assess relevant commercial data sources (e.g., Komodo Health, IQVIA, Symphony) that can be integrated into analytics projects to improve insights and decision-making capabilities.
- Customer Segmentation: Apply advanced data science techniques to develop customer segmentation strategies that optimize targeting and resource allocation for marketing and sales teams.
- Engagement Optimization: Analyze customer behavior and market data to develop insights that enhance engagement strategies and drive commercial success.
- National-Level Reporting: Develop and maintain national-level brand performance reports, providing detailed insights into market trends, competitive dynamics, and product performance.
- Launch Support: Provide analytical support during product launches, including competitive intelligence, market analysis, and performance tracking to ensure successful market entry.
- Operational and Market Effectiveness: Analyze data to inform operational effectiveness and market performance, providing insights that optimize marketing strategies and sales operations.
- Reporting and Dashboards: Create and manage executive-level reports and dashboards, ensuring that key performance indicators (KPIs), market trends, and insights are effectively communicated to decision-makers.
- Cross-Functional Partnership: Work closely with other teams (e.g., Marketing, Market Research Insights, Forecasting, Finance) to integrate data insights into broader strategic initiatives and support decision-making.
- Stakeholder Communication: Present complex analytical insights to senior leadership, translating data into clear and actionable recommendations that support business-critical decisions.
- Process Improvement: Continuously identify opportunities to improve data analytics methodologies, processes, and tools to enhance the efficiency and accuracy of insights.
- Emerging Trends: Stay current with industry trends and emerging data science techniques, incorporating innovative approaches into data analysis to drive better business outcomes.
- Vendor Collaboration: Manage relationships with external vendors and data providers, ensuring timely delivery of high-quality data and insights that meet the franchise’s needs.
- Budget and Resource Allocation: Collaborate with Data Science & Analytics leadership to manage budgets and allocate resources effectively to support franchise data science and analytics efforts.
- Education: Bachelor’s degree in data science, analytics, mathematics, biological or physical sciences, statistics, business, or a related field.
- Experience: Minimum of 6+ years of experience in data science, analytics, or commercial operations, with at least 5+ years in the pharmaceutical or biopharmaceutical industry. Post-graduate education can substitute for a portion of the work experience requirement.
- Technical Skills: Proficiency in data analysis tools such as Python, R, SQL, and data visualization platforms (e.g., Tableau, Power BI).
- Cross-Functional Experience: Experience working with cross-functional teams, including marketing, sales, and IT, to integrate data insights into commercial strategies.
- Oncology Expertise: Experience working in oncology, with a deep understanding of market dynamics, customer behavior, and competitive trends in the oncology space.
- Technical Skills: Experience with machine learning and predictive modeling techniques is highly desirable.
- Advanced Degree: MBA or advanced degree in a related field is preferred.
- Analytical Mindset: Strong analytical and problem-solving skills, with the ability to develop data-driven solutions to complex business challenges.
- Strategic Thinking: Ability to think critically and strategically, translating complex data into actionable forecasts that drive business decisions.
- Communication: Excellent verbal and written communication skills, with the ability to present complex data insights to commercial leaders and cross-functional teams in a clear and actionable manner
- Project Management: Proven ability to manage multiple projects simultaneously, prioritize competing demands, and meet deadlines in a fast-paced environment.
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