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Tier-1 employer, metro location, and mid-level generalist data role create high competition.
Technical data science skills are transferable, but life-sciences domain experience is preferred.
Explicit 6–8 years plus mandatory causal analytics, Python, SQL, and managerial experience.
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Lead causal inference and impact measurement to evaluate commercial and customer engagement strategies.
Apply advanced statistical methods and experimentation to quantify incremental business impact and inform decision-making.
Develop attribution frameworks across field and digital channels and mentor data scientists in best practices for causal analytics.
6–8 years experience in causal inference, experimentation, marketing science, commercial analytics, or data science.
Strong expertise in statistical modeling, experimental design, and impact measurement.
Proficiency in Python, SQL, and large-scale analytics environments.
Ability to communicate complex analytical findings to business stakeholders.
Experienced in commercial analytics focusing on impact measurement and causal inference methods for marketing effectiveness.
Skilled in applying advanced statistical and experimental design techniques within data science teams.
Comfortable working at the intersection of data science and business partnering to translate analytical insights into decisions.