Principal Data Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Data Scientist based in the United States. 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.

Define and own the multi-year data science vision and technical roadmap for omnichannel engagement analytics, anticipating emerging business needs and developments in AI. Identify, assess, and prioritize opportunities where AI and advanced analytics can deliver measurable commercial value, develop business cases, and influence senior leadership investment decisions. Build and prototype generative AI solutions using LLM APIs, AI-assisted development tools, agentic frameworks, advanced retrieval-augmented generation (RAG), and context engineering. Develop rigorous scientific evaluation frameworks to validate generative AI systems and deliver reliable, scalable omnichannel recommendations. Design and implement predictive, causal, recommendation, next-best-action, and sequential decision models using complex, multi-source datasets to improve customer engagement and resource allocation. Establish measurement strategies for effectiveness, incrementality, and business impact using A/B testing, quasi-experimental methods, and other robust experimental approaches. Partner with product owners to define business problems, clarify desired outcomes, establish success metrics, and translate strategic objectives into scalable technical solutions. Build alignment across marketing, brand, field sales, operations, medical, legal, regulatory, compliance, technology, and external vendor teams, resolving competing priorities and reframing ambiguous requests into actionable analytical problems. Communicate complex scientific methods, model findings, and business implications through clear recommendations and compelling presentations tailored to executive and non-technical audiences. Collaborate with engineering, platform, and technology teams to deploy, monitor, maintain, and manage the lifecycle of AI and machine learning solutions in production environments. Establish and promote best practices in model development, code quality, reproducibility, peer review, scientific validation, and responsible AI. Mentor data scientists and contribute to technical discussions, analytical debate, and the broader data science community without formal direct-report responsibilities. Lead technical workstreams involving external analytics vendors and partners, ensuring the quality, scientific rigor, and methodological soundness of their deliverables. Stay current with advances in machine learning, generative AI, causal inference, and optimization, introducing innovative approaches and contributing to internal and external thought leadership. Requirements: 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. Benefits: Comprehensive benefits package: Medical, dental, and vision insurance for eligible employees. Retirement planning: Access to a 401(k) plan for eligible employees. Paid time off: Vacation, holidays, and sick leave. Long-term incentives: Eligibility to participate in long-term incentive programs. Compensation: Base pay determined by factors such as geographic location, experience, and job grade. The source description does not provide a specific salary range. Professional impact: Opportunity to influence enterprise AI strategy and develop production-grade solutions that connect advanced analytics to measurable business outcomes. Technical leadership: Scope to mentor colleagues, shape scientific standards, and influence cross-functional technical decisions without formal direct-report responsibilities. Inclusive workplace: Equal opportunity employment and reasonable accommodation support during the recruitment process.