AI & Real World Analytics Senior Manager

Role Summary

The AI and Real-World Analytics Senior Manager will play a pivotal role in leading and advancing the RWA team’s AI and automation capabilities. This role will focus on driving the development and optimization of the real world data analysis processes. The position will involve deep diving into Real-World Data (RWD) to generate data expertise, actionable insights, and optimal data use strategies to support business objectives. The position is responsible for identifying opportunities within the team to enhance automation, standardization, and simplification and partnering with leadership to prioritize them. Then they will gather requirements, and either build agents using enterprise low/no code solutions or partnering with Data, Technology, and Engineering (DTE) to develop and deploy more advanced AI solutions.

Responsibilities

• Lead the design, execution, and delivery of Real-World Evidence (RWE) analysis projects, incorporating AI/ML methodologies
• Optimize and complete processes such as analytics project workflows and data source intake and hosting workflows by identifying inefficiencies and implementing AI/ML-driven solutions in partnership with DTE.
• Streamline project management processes, including request handling, execution, and delivery, to automate task tracking, resource allocation, and communication at key milestones.
• Enhance and standardize process documentation, utilizing AI/ML algorithms to identify common gaps in analytic programs and recommend optimal practices for improved outcomes.
• Identify automation opportunities for tasks like database analysis plan (DAP) validation, medical code list searching, programming plan and code generation, quality control, and result cross-checking. Manage and support internally-developed AI solutions, including access, runbooks, and operational workflows.
• Apply advanced AI/ML techniques to generate deep insights from real-world data (RWD), including benchmarks, representativeness, gaps, and overlaps, and propose innovative solutions to address data capability limitations.
• Partner with the Digital Technology and Engineering (DTE) team to understand the company’s AI/ML environment and recommend innovative tools and approaches to improve the RWA team’s efficiency and analytical capabilities. Work closely with DTE AI Engineering and AI Operations to establish and implement best practices, standards, and observability.
• Work with DTE to design and execute comprehensive AI/ML model validation strategies, including data splitting, metric selection, cross-validation, robustness testing, fairness audits, and post-deployment monitoring.
• Experienced in providing training and technical support to end users for effective adoption and utilization of AI and automation solutions.
• Shapes the technical strategy of the Real-World Analytics group by providing thought leadership, guiding the adoption of cutting-edge technologies, and encouraging a culture of creativity and continuous improvement within the team.
• Strong understanding of Generative AI ethics and governance frameworks: knowledge of ethical considerations, responsible AI practices, and governance frameworks related to the development, deployment, and use of Generative AI tools, including awareness of biases, data privacy concerns, transparency, accountability, and regulatory compliance.

Qualifications

• Strong knowledge and hands-on experience in data science and AI/ML methodologies, including supervised, unsupervised, and reinforcement learning, as well as machine learning algorithms such as neural networks and random forests.
• Experience designing, deploying, and supporting AI applications using AWS, GCP, and Azure engineering ecosystems, with specific expertise in Azure AI Foundry.
• Proficiency in statistical programming languages such as Python, SQL, SAS, and R, with familiarity in working within AWS computational environments.
• Knowledge of healthcare claims and electronic health record (EHR) data, with exposure in major U.S. real-world data (RWD) sources.
• Knowledge of statistical modeling and observational research.
• Strong problem-solving and analytical abilities.
• Excellent communication skills for collaborating with stakeholders and bridging technical and domain teams.
• Experience working in fast-paced environment.
• Minimum 5 years of experience working in observational research within the life sciences industry or relevant academic, government, or consulting environment, with at least 3 years of experience in AI/ML related data engineering and automation.

Education

• Advanced degrees in epidemiology, biostatistics, math, data science, or similar disciplines required