Top Senior Data Science Jobs in Rochester, NY

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.

As a Senior Data Scientist, you will design and deploy machine learning and graph algorithms to enhance identity trust solutions, own end-to-end projects, and collaborate with product and engineering teams to improve KYC and compliance products.

Lead the design and deployment of machine learning and graph-based systems for international identity verification, ensuring compliance and addressing regional challenges.

Lead the development and deployment of advanced ML/AI products, mentoring teams and collaborating with stakeholders to influence business outcomes.

The Senior Data Scientist in Conversational AI & NLU designs and optimizes AI solutions, evaluates models, collaborates with stakeholders, and analyzes conversational data to improve user experience.

Lead development of rigorous evaluation frameworks and statistical methods to measure and validate autonomous vehicle system performance. Monitor metric reliability, detect drift, identify edge cases, correlate simulation and on-road results, and feed scenarios into simulation catalogs. Partner with safety, systems, and engineering teams to inform launch decisions, build self-service tooling for developers, and mentor peers. Introduce scalable ML methods where appropriate to improve evaluation rigor and coverage.

Lead technical roadmap and delivery of ML and GenAI capabilities for retail pricing, demand forecasting, promotion effectiveness, and markdown recommendations. Architect GenAI agents and set model/MLOps standards, mentor distributed data science teams, partner with product and engineering, and present AI strategy to executives and customers.

Lead development and operationalization of measurement products (MMM, incrementality, insights, recommendations). Build, deploy, and maintain production-grade statistical and ML systems, debug end-to-end data and model issues, evaluate methodologies, and partner with engineering, product, and customer teams to improve measurement accuracy and reliability. Provide customer-facing explanations and support for measurement outputs.