Principal Data Scientist

The Principal Data Scientist will define the scientific and technical direction of advanced AI-driven omnichannel engagement analytics, helping transform complex data into measurable commercial impact. This senior individual-contributor role combines deep expertise in machine learning, generative AI, causal inference, and experimentation with strategic business leadership. You will develop personalized recommendations and decision-optimization models that improve customer engagement, channel effectiveness, and resource allocation. Working closely with commercial, product, engineering, and regulatory stakeholders, you will identify high-value opportunities and turn ambitious ideas into scalable production solutions. You will also establish rigorous standards for model evaluation, responsible AI, and scientific reproducibility. This is an opportunity to influence enterprise-wide AI strategy, mentor data science talent, and advance innovation in a complex, highly regulated environment.

• Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline; a master’s degree or PhD in a quantitative field is strongly preferred.

• At least 8 years of experience in data science, machine learning, or applied science, or 6+ years with a PhD, with a proven track record of delivering production-quality models that generate measurable business outcomes.

• Demonstrated ability to establish technical direction across a portfolio of AI/ML solutions and influence strategic decisions made by senior business leaders.

• Experience developing personalization, recommendation, decision-optimization, or marketing-science solutions in any industry.

• Deep practical expertise in causal inference and experimentation, including A/B testing and quasi-experimental methods for measuring business impact.

• Hands-on experience using generative AI and AI-assisted development tools, such as LLM APIs, AI coding assistants, and agent frameworks, with concrete examples of prototypes or solutions that can be discussed during interviews.

• Expert proficiency in Python and SQL, along with experience using deep learning frameworks such as PyTorch or TensorFlow and distributed data processing technologies.

• Experience deploying and operating AI/ML solutions in cloud environments, including production monitoring, maintenance, and lifecycle management.

• A demonstrated record of leading initiatives across complex, cross-functional stakeholder environments and presenting technical recommendations to executive audiences.

• Strong scientific and engineering discipline, including reproducibility, model evaluation, production readiness, and enterprise integration.

• Excellent business judgment, problem-framing, communication, and stakeholder management skills, with the ability to turn ambiguous challenges into well-defined analytical solutions.

• Preferred: Commercial analytics experience in pharmaceutical, biotechnology, healthcare, or life sciences, including familiarity with IQVIA, Symphony Health, APLD, or similar prescription, claims, and engagement data sources.

• Preferred: Experience with omnichannel analytics, customer journey analysis, touchpoint measurement, closed-loop measurement, or next-best-action solutions for healthcare professional (HCP) or patient engagement.

• Preferred: Knowledge of advanced machine learning and optimization methods, including reinforcement learning, multi-armed bandits, transformers, and sequential recommendation techniques.

• Preferred: Experience building LLM-powered and agentic AI systems using orchestration frameworks, evaluation methods, APIs, and production pipelines.

• Preferred: Familiarity with cloud machine learning platforms and MLOps tools such as Azure ML, Databricks, and MLflow.

• Preferred: Evidence of industry thought leadership through conference presentations, publications, patents, open-source contributions, or other recognized contributions.

• Preferred: Experience working in Agile product development environments.