1. Experiment Design & Methodology :

- Design and operationalize A/B tests, multivariate tests, and quasi- experimental designs across pharma omnichannel campaigns (HCP digital, email, rep detailing, remote engagement, media).

- Define test hypotheses, success criteria, treatment/control structures, and eligibility rules aligned to brand and business objectives.

- Select and apply appropriate causal inference methods (e.g., randomized control, propensity score matching, diff- in- diff) when true randomization is not feasible.

- Ensure experiments comply with pharma- specific constraints (no patient- level targeting, rep allocation rules, ethical considerations).

2. Analytics, Modeling & Statistical Rigor :

- Conduct statistical analysis of experiment results, including power analysis, confidence intervals, significance testing, and effect size estimation.

- Build and maintain campaign effectiveness models leveraging regression, Bayesian methods, or uplift modeling as needed.

- Interpret results with a focus on business relevance, not just statistical significance.

- Validate assumptions, handle biases, and document methodological limitations clearly.

3. Insights Generation & Storytelling :

- Translate complex analytical findings into clear, action- oriented insights for brand teams, omnichannel leads, and commercial leadership.

- Create experiment read- outs, test scorecards, and executive- ready summaries focused on ROI and optimization levers.

- Recommend next- best actions (scale, optimize, stop, retest) based on evidence.

- Contribute to codifying test- and- learn best practices across brands and markets.

4. Stakeholder Collaboration & Consulting Mindset :

a. Act as an analytics partner to omnichannel, marketing, digital, and field operations teams.

b. Support campaign planning by advising on "what to test", expected lift ranges, and measurement feasibility.

c. Educate non- technical stakeholders on experiment interpretation, caveats, and confidence levels.