Top Hybrid Data Science Jobs

Leads data science and AI/ML initiatives for semiconductor manufacturing, including yield analysis, yield debugging, failure prediction, anomaly detection, forecasting, and automated root cause analysis. Builds and deploys AI agents and predictive models, analyzes manufacturing and test data, collaborates with hardware and process engineering teams, and communicates insights through visualizations and presentations.

Leads data science and advanced analytics initiatives supporting sales effectiveness, portfolio performance, forecasting, incentives, and growth. Develops predictive, statistical, machine learning, NLP, and GenAI solutions, including LLM and RAG applications. Builds scalable data pipelines and models using distributed computing and cloud patterns, leads data scientists, defines performance metrics, and communicates insights to executives and cross-functional stakeholders.

Develop partner-specific pricing strategies for products and bundles, analyze volume, conversion, cost, and competitive data, and identify revenue opportunities. Build AI-powered pricing calculators, deal-sizing models, dashboards, and experimentation frameworks. Collaborate with Finance, Partnerships, Product, Compliance, and Legal to ensure commercially sound pricing. Establish scalable pricing frameworks, reduce escalations, document decisions, and communicate complex pricing recommendations to senior stakeholders and non-technical partners.

Lead computational platform development for biological design, optimization, and functional prediction. Collaborate with wet-lab scientists to validate predictions, oversee data infrastructure and computational workflows, and contribute to technology roadmaps, funding proposals, and partnerships. The role requires developing machine learning methods for biological sequence and structure analysis, translating biological problems into computational solutions, and leading technical teams across computational and experimental disciplines.

Curate and annotate investment datasets from infrastructure and private credit virtual data rooms, identify financial, legal, technical, and operational deal terms, and draft investment memos for AI model training and evaluation. Define annotation standards, manage labeling quality control, collaborate with AI/ML, product, and investment teams, and contribute to prompt engineering and AI-generated diligence tools.

Senior Data Scientist responsible for defining product metrics, designing and evaluating experiments, analyzing growth, retention, and monetization, and building trusted datasets, dbt models, and dashboards. Partners with Product, Engineering, Design, and Marketing to guide roadmap decisions, establish analytical standards, communicate insights to leadership, and mentor teammates.

Summer intern will build semantic data models, develop dbt pipelines, prepare raw data for analytics and AI agents, optimize scripts and workflows, and collaborate with data scientists, engineers, and business stakeholders. The role requires SQL and Python scripting, reporting-tool experience, and the ability to learn NLP coding in Snowflake and dbt. It is a full-time hybrid position with one on-site day weekly in Newark, Ohio.

Develop and productionize machine learning models for Sportsbook risk management, including pricing accuracy, betting limits, and customer profiling. Responsibilities include data collection and cleaning, feature and model selection, predictive modeling, production coding, dashboard and visualization development, and communicating insights to product, risk, trading, and other stakeholders.

Lead end-to-end machine learning and AI solutions for transaction monitoring and Wealth Management analytics. Partner with product users and stakeholders to identify needs, translate business problems into technical requirements, analyze complex data, and develop, validate, deploy, and support production models. Collaborate globally while evaluating generative AI opportunities and addressing product, business, and regulatory requirements.

Senior Data Scientist partnering with Product and Customer Support Operations to improve marketplace decisions and performance. Responsibilities include exploratory analysis, root-cause investigations, experimentation, causal analysis, production machine learning model ownership, metric and dashboard development, self-service reporting, and LLM-assisted analysis. The role requires strong Python and SQL skills, BI tool experience, applied machine learning expertise, and the ability to influence product and operational roadmaps.