Top Hybrid Data Science Jobs

Leads advanced analytics, artificial intelligence, machine learning, forecasting, optimization, and scenario modeling for electric utility transmission and distribution planning. Analyzes complex utility datasets, develops dashboards and decision-support tools, supports grid modernization and large-load integration, advises clients, communicates findings to technical and executive audiences, mentors staff, and contributes to business development and thought leadership.

Develops and evaluates machine learning and generative AI solutions, builds proof-of-concept applications, integrates data from multiple sources, creates modeling-ready datasets, constructs predictive models, and communicates actionable insights to leadership. The role also supports automation, solution integration, mentoring junior data scientists, and collaboration with cross-functional teams. This hybrid campus-based position requires onsite work three days per week.

Build data models, governed metrics, experiments, causal analyses, predictive models, and AI evaluation systems. Connect product and business data to measure enterprise customer value, develop internal AI tools, investigate metric changes, and provide strategic recommendations. The role partners with engineering, mentors analysts and data scientists, and supports analytics across product, sales, marketing, and finance in a hybrid environment.

Leads commercial data science and advanced analytics for a biopharmaceutical company. Builds and deploys machine learning, predictive, causal, experimentation, and AI solutions using healthcare, claims, sales, payer, patient, and digital data. Partners with marketing, sales, market access, finance, IT, and operations to generate insights, optimize promotional investments, improve patient and customer journeys, automate reporting, and support strategic decisions. Communicates technical findings and recommendations to executive and cross-functional stakeholders.

Develop pricing and rider-engagement algorithms, including incentive, budget-management, and real-time optimization systems. Build and deploy statistical, machine-learning, and optimization models in production using Python. Design experiments, establish product metrics, analyze marketplace outcomes, and identify business opportunities. Partner with Product, Engineering, Analytics, business teams, and executives to frame problems, guide roadmaps, and communicate data-driven recommendations.

Leads Coherent’s data science and analytics strategy, team, platform, and governance. Oversees production machine learning, predictive modeling, forecasting, process optimization, MLOps, data infrastructure, and business intelligence across manufacturing, supply chain, and commercial operations. Partners with executives and cross-functional leaders to deliver measurable business value, establish data quality and model-risk standards, and evaluate generative AI and LLM applications.

Develop predictive, prescriptive, forecasting, causal, optimization, and machine learning solutions for R&D, quality, regulatory, consumer insights, product development, packaging, and manufacturing. Build and monitor Python models, create analytical products and decision-support tools, establish measurement frameworks, and translate complex analyses into practical recommendations. Collaborate with R&D, AI, data engineering, quality, and regulatory stakeholders to productionize solutions, ensure responsible data use, drive adoption, and deliver measurable business impact.

Lead end-to-end data science and machine learning solutions, translating business problems into production-ready models, GenAI applications, and reusable technical patterns. Establish evaluation, governance, monitoring, documentation, and quality standards while mentoring data scientists and ML engineers. Collaborate with senior stakeholders, data engineering, platform teams, and distributed onshore/offshore groups to deliver maintainable solutions across statistical modeling, forecasting, NLP, optimization, and responsible AI.

Leads the organization’s data science function by hiring, managing, and mentoring data scientists. Oversees advanced analytics and machine learning initiatives from problem definition through deployment and monitoring. Partners with IT, product, executives, and business stakeholders to prioritize solutions, source and prepare data, advise on methods, and deliver scalable enterprise applications with measurable business impact.

Leads the organization’s data science function by hiring, managing, and mentoring data scientists; overseeing advanced analytics and machine learning initiatives; and guiding solutions from ideation through deployment and monitoring. Partners with IT, product, executives, data management teams, and business stakeholders to develop scalable enterprise solutions, solve operational challenges, ensure technical quality, and communicate analytical methods and business impact.