Top Principal Data Scientist Jobs

As a Data Scientist, collaborate with teams to build machine learning models using large datasets, translating complex analyses into actionable insights for customer-centric products.

As a Principal Associate, Data Scientist, you will develop machine learning models and analytics to combat money laundering, collaborating with a cross-functional team and modernizing risk management practices.

The Data Scientist will collaborate with teams to develop AI features, build ML models, and translate complex data insights into business goals, leveraging technologies like Python and AWS.

The Principal Data Scientist will analyze data, develop predictive models, and apply AI/ML to enhance healthcare quality and outcomes.

The Principal Data Scientist leads advanced machine learning initiatives, designs scalable AI solutions, and shapes Aera's data science strategy while collaborating with cross-functional teams.

Design and build high-performance AI infrastructure, optimizing and maintaining production systems while collaborating with researchers and analysts.

As a Principal Data Scientist, you'll design and deploy cutting-edge machine learning models to drive financial success for customers and guide technical teams.

Lead the strategy and design of GenAI and agentic AI solutions. Oversee development and deployment of AI systems, ensuring scalability and reliability while fostering innovation and collaboration across teams.

Lead the architecture, development, and delivery of generative AI solutions. Collaborate across teams to translate requirements, monitor performance, and build scalable ML pipelines.

The Senior Research Engineer will curate large datasets, automate data pipelines, optimize queries, and support data-driven research impacting business outcomes.

The role involves developing and deploying machine learning models for anti-money laundering, leveraging advanced analytics, statistics, and large datasets while collaborating with cross-functional teams.

The Principal Data Scientist will enhance predictive models for healthcare quality management and operational efficiency, analyzing multiple data sources and applying AI and machine learning to improve outcomes.

Lead design, development, and maintenance of fraud-risk and predictive models, architect ETL and storage for large datasets, manage analytic projects and teams, document solutions, research new techniques, and communicate technical results to stakeholders.

The Principal Data Scientist leads AI innovation initiatives, focusing on prototyping, evaluating AI systems, and translating research into enterprise products, while mentoring teams and shaping Genentech's AI capabilities.

Lead end-to-end design, development, and deployment of ML models (churn, retention, LTV), drive causal inference and experimentation for targeting and bidding, partner with Product and Engineering to define metrics, instrument experiments, operationalize models, and mentor analytics teams to influence product roadmaps and personalized customer journeys.

Develop and maintain predictive models for quality, risk adjustment, and operational efficiency; analyze multi-source healthcare data; implement ML solutions, monitor model performance, and experiment with emerging AI to improve outcomes and workflows.

The role involves partnering with teams to deliver AI products, leveraging technologies for insights, and building machine learning models, focusing on NLP and LLMs in a production environment.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.

The Sr Data Scientist, Risk will analyze fraud patterns, utilize machine learning techniques, develop models, and collaborate with teams to mitigate fraud in crypto trading.

The role involves analyzing fraud patterns, developing machine learning models, and collaborating with stakeholders to enhance investigation processes for risk management.

Lead the technical strategy for search and recommendation systems, develop models, and improve business outcomes through data science expertise.

Lead and execute applied AI/ML projects: translate ambiguous business problems into technical approaches, architect and validate models and pipelines, engage clients, mentor small teams, ensure code and deliverable quality, and contribute to business development and technical strategy.

The Principal Data Scientist leads AI solutions, develops machine learning models, collaborates with stakeholders, and drives innovation in analytics and data science.

The role involves designing and optimizing data pipelines for TDS, ensuring data quality and compliance, and collaborating across teams for data-driven decision making.

The Principal Clinical Data Scientist leads clinical data management for complex studies, ensuring data quality, compliance, and timely delivery of milestones while collaborating across teams to address data-related issues and support analytics innovation.