Responsibilities

• Partner with marketing and business stakeholders to define analytical problems, success criteria, data requirements, and measurable business outcomes.
• Develop machine learning and statistical models, including approaches such as regression, classification, clustering, attribution modeling, marketing mix modeling, lead scoring, causal inference, and controlled marketing experiments.
• Support the full model lifecycle, including feature engineering, model validation, documentation, deployment support, performance monitoring, and ongoing refinement.
• Communicate insights, model outputs, and recommendations clearly to both technical and non-technical stakeholders.
• Lead the vision, design, and delivery of AI-enabled products and self-service analytics platforms that improve insight accessibility, workflow efficiency, and enterprise decision-making.
• Develop and operationalize GenAI and LLM-powered applications, including conversational analytics interfaces, automated insight generation, metadata enrichment, intelligent recommendation systems, and AI-assisted decision support tools.

Requirements

• Master’s degree in data science, Statistics, Economics, Computer Science, Engineering, or a related field.
• 8+ years of experience in data science, clinical analytics, health care analytics or a related analytical role.
• Advanced proficiency in Python and SQL, with experience working in cloud-based analytics and machine learning environments.
• Experience developing statistical or machine learning models using Python and related frameworks, such as scikit-learn, statsmodels, XGBoost, PyMC-Marketing, or Google Meridian.
• Strong analytical thinking and ability to translate business problems into data-driven analytical solutions and communicate technical concepts and findings clearly to business stakeholders.
• Demonstrated ability to collaborate across business and technical teams.

Preferred

• Experience with Snowflake or similar analytics platforms.
• Experience working with health care, medical device or other highly regulated data environments.
• Experience working with health insurance claims data, patient diagnosis data, physician notes and prescription data.
• Familiarity with MLOps and model lifecycle practices, including data preparation, feature engineering, validation, documentation, deployment support, monitoring and model maintenance.
• Experience with marketing analytics methods, such as marketing mix modeling, attribution modeling, incrementality measurement, lead scoring, targeting or campaign optimization.
• Experience working in a Fortune 500 company, particularly within regulated industries or environments involving sensitive data, such as financial services or health care.