Senior Manager, Commercial Analytics

Overview of Role:

The Senior Manager, Commercial Analytics is the person who gets into the data and finds what is actually happening. This role mines the Company’s  internal commercial data and syndicated secondary data to produce insight the business can act on. Internal data means CRM activity, field reporting, specialty distributor and specialty pharmacy channel files, hub and patient services data, and the commercial data warehouse. Syndicated secondary data means purchased outlet level data, diagnosis and medical claims, patient-level longitudinal data, biomarker testing data, and oncology reference and affiliation data.

The role sits below the Director, Commercial Analytics & Information and is the primary hands-on analyst for the function. Because ivonescimab is an intravenous, buy-and-bill product, standard prescription data will not describe demand. This role must build a credible picture of performance from purchase, claims, and channel data, and be explicit about what that picture can and cannot show. The successful candidate does not wait to be handed a question. They find the pattern, work out whether it matters commercially, and bring it forward with a point of view.

Role and Responsibilities:

• Mine internal commercial data for insight, including CRM activity, field call and engagement data, specialty distributor and specialty pharmacy channel files, hub and patient services data, and the commercial data warehouse.
• Mine syndicated secondary data for insight, including outlet level purchase data, diagnosis and medical claims, patient-level longitudinal data, biomarker testing data, and prescriber and organization reference and affiliation data.
• Produce recurring performance analysis covering national, regional, and account level results, market share, and attainment against plan, and explain the drivers rather than only reporting the numbers.
• Analyze performance by customer segment, site of care, treatment setting, and treatment pattern, and identify where results diverge from expectation and why.
• Identify geographic variation in performance and translate it into specific, actionable recommendations for Sales Operations and the field.
• Build and maintain the analytical view of patient population, including biomarker testing and retesting rates, testing turnaround, treatment sequencing, and where eligible patients are being lost before treatment.
• Analyze the patient journey end to end, including referral and diagnosis, biomarker testing, treatment initiation, benefits verification and access, time to first infusion, persistence, and discontinuation.
• Analyze hub, patient services, and specialty pharmacy data to identify access and reimbursement friction and quantify the commercial impact of that friction.
• Support the customer targeting and segmentation approach with analysis of account potential, prescriber behavior, referral networks, and organizational affiliation, in partnership with Sales Operations and Marketing.
• Provide the analytical inputs to the commercial demand forecast, including patient segment sizing, treatment share trend analysis, and the historical basis behind assumptions.
• Build and maintain dashboards and self-service reporting in the business intelligence platform, designed for decisions rather than for data display.
• Proactively identify data anomalies, breaks, and quality problems in analytical output, investigate root cause, and escalate accordingly with a clear description of the impact.
• Document the definitions, logic, filters, and data sources behind every recurring analysis so another analyst can reproduce and audit the result.
• Analyze competitive performance and treatment landscape shifts in non-small cell lung cancer, and flag changes that carry commercial consequence.
• Synthesize secondary data findings with primary market research and field intelligence into a single explanation rather than three competing ones.
• Present findings to Commercial Operations, Sales, Marketing, and Market Access in a language a commercial audience can act on without an analytics background.
• All other duties as assigned.

Experience, Education and Specialized Knowledge and Skills:

• Bachelor's degree in a quantitative, business, or life sciences discipline. Master's degree preferred.
• Minimum of 5+ years of commercial analytics experience in pharmaceuticals or biotechnology, or in a consulting firm serving that industry.
• Oncology experience is strongly preferred. Experience supporting a lung cancer brand is a distinct advantage.
• Direct hands-on experience with pharmaceutical secondary data is required, including outlet level purchase data, medical and diagnosis claims, and patient-level longitudinal data.
• Experience with intravenous, buy-and-bill products and the data limitations that come with them is strongly preferred. Candidates whose experience is limited to retail prescription data will find this role difficult.
• Familiarity with biomarker-driven market segmentation and what it means commercially.
• Familiarity with hub, patient services, and specialty pharmacy data, and with access and reimbursement metrics for specialty oncology products.
• Experience providing data inputs to commercial forecasting, including patient segment sizing and share trend analysis.
• Proficiency in SQL is required. Proficiency in Python or R for querying, manipulating, and automating analysis on large datasets is strongly preferred.
• Experience working in a cloud data environment such as Snowflake or equivalent is preferred.
• Strong skills in a business intelligence platform such as Power BI or Tableau, with a focus on clean, decision-oriented design rather than decoration.
• Advanced Excel skills, including the ability to build a model another analyst can audit.
• Demonstrated ability to work independently, identify the question worth asking, and bring a point of view rather than waiting for assignment.
• Excellent written and verbal communication skills, with a track record of presenting analytical findings to senior stakeholders.
• Ability to manage several projects and stakeholders with competing deadlines at the same time.
• Comfortable operating in ambiguity at a company building its commercial analytics capability for the first time.