Senior-BC sector - LS -Commercial analyst

Senior Consultant – Commercial Analytics – Life Sciences

As a Senior Consultant in LS Commercial Analytics, you will work on real world commercial problems that biopharma organizations face across brand, sales, field excellence, patient analytics, and omnichannel engagement. You will be expected to bring hands on experience working with pharmaceutical datasets and a strong understanding of how commercial decisions are made across the drug lifecycle.

This role requires someone who can not only run analyses but also understand the business logic behind them - why certain KPIs matter, how incentives influence field behaviour, what drives patient drop off, and how digital and field channels complement each other.

Roles and Responsibilities

• Core Commercial Analytics
  
  
  
  • Work with syndicated datasets (e.g., IQVIA DDD/LRx/NPA/Xponent, longitudinal claims, specialty pharmacy data) and internal CRM/digital datasets to answer brand critical questions.
  
  
  • Perform analyses across the commercial value chain, such as:
    
    
    
    • Patient journey analysis (diagnosis, initiation, titration, adherence, switch/drop off).
    
    
    • Sales performance deep dives (volume drivers, new-to-brand trends, payer restrictions impact).
    
    
    • Field force analytics (call plan optimization, target lists, trigger-based engagement, call quality assessment).
    
    
    • Omnichannel analytics (channel effectiveness, promotional mix, sequencing, engagement scoring).
    
    
    
    
  
  
  • Translate raw signals from data into brand ready narratives that shape tactical decisions (targeting refinements, messaging prioritization, pull through strategies).
  
  
  
  


• Measurement Strategy & KPI Frameworks
  
  
  
  • Build or refine KPI frameworks specific to pharma commercial models (e.g., NBRx vs TRx dynamics, HCP engagement funnels, patient pull through metrics).
  
  
  • Define attribution logic for multi-channel campaigns and field-digital orchestration.
  
  
  • Create reusable data/analytics assets aligned to therapy area nuances—oncology, immunology, rare disease, primary care, etc.
  
  
  
  


• Data Operations & Quality
  
  
  
  • Validate claims data anomalies such as data lag, panel changes, or payer mix shifts.
  
  
  • Understand nuances of specialty distribution, channel leakage, and hub services to ensure clean downstream analytics.
  
  
  • Partner with data engineering to shape data models that reflect the commercial ecosystem (HCP, account, patient, payer linkages).
  
  
  
  


• Stakeholder & Business Partnering
  
  
  
  • Act as an analytics partner to Brand Leads, Commercial Excellence, Sales Leadership, Omnichannel Teams, and Medical/Market Access stakeholders.
  
  
  • Participate in brand planning cycles by generating insights that influence forecasts, targeting, messaging, and multichannel tactics.
  
  
  • Develop clear dashboards and story driven presentations tailored to commercial audiences (brand managers think differently from sales directors—your outputs must reflect that).
  
  
  
  


• Desired Skills & Experience
  
  
  
  • 3–6 years of hands on experience in Pharma Commercial Analytics, preferably working with biopharma brands or commercial teams.
  
  
  • Strong working knowledge of industry datasets such as:
    
    
    
    • IQVIA: Xponent, DDD, NPA, NSP, LAAD, LRx
    
    
    • Claims datasets (Komodo/DRG/Truven/IQVIA/LAAD)
    
    
    • Specialty pharmacy/HUB data
    
    
    • Veeva CRM & digital interaction data
    
    
    
    
  
  
  • Experience in at least 2–3 analytic domains:
    
    
    
    • Patient journey and claims analytics
    
    
    • HCP segmentation and targeting
    
    
    • Field performance analytics
    
    
    • Sales forecasting & demand planning support
    
    
    • Omnichannel & campaign analytics
    
    
    • Incentive compensation
    
    
    
    
  
  
  • Strong technical skills: SQL or Python/R for data wrangling, plus good to have visualization skills (Tableau/Power BI).
  
  
  • Ability to convert analytic findings into commercial recommendations.
  
  
  • Good understanding of pharma commercial models, promotional constraints, payer dynamics, and therapeutic area nuances.
  
  
  • Comfortable operating independently in fast-moving environments where requirements may evolve.