Top Hybrid Data Science Jobs

Lead the design, development, deployment, and maintenance of machine learning models for personalization, recommendations, churn prediction, lifetime value, advertising, and business insights. Own the full data science lifecycle, analyze complex datasets, collaborate with product, engineering, editorial, and revenue teams, mentor junior staff, and promote advanced data science practices.

Leads Innovative Medicine Supply Chain’s decision science strategy, roadmap, governance, technical innovation, and capability development. Oversees a team of more than 10 managers and data scientists delivering optimization, simulation, forecasting, AI, machine learning, and advanced analytics solutions. Partners with business and technology leaders to build scalable digital products, establish model standards, develop talent, and connect technology investments to supply chain outcomes in a regulated pharmaceutical environment.

Own end-to-end data science and AI evaluation projects, developing frameworks, metrics, golden datasets, benchmarks, pipelines, and dashboards for LLM- and speech-based systems. Analyze failure modes, calibrate LLM judges against human ground truth, and drive continuous product improvement. Collaborate with AI/ML engineers and product managers, contribute to technical strategy, mentor junior data scientists, and establish evaluation best practices.

Develop and improve trustworthy quarterly user-growth forecasts at Duolingo. Collaborate with Product, Finance, Strategy, leadership, and other data scientists to translate business and product changes into analytical requirements and actionable recommendations. Validate forecasting methodologies, build robust repeatable forecasting processes, communicate complex findings clearly, and mentor junior data scientists. The role requires strong quantitative expertise, real-world forecasting experience, and fluency in SQL, Python, and analytics engineering ecosystems.

Analyze healthcare revenue cycle data to generate actionable insights, improve product design and user experience, and support data quality. Responsibilities include data preparation, transformation, validation, visualization, statistical analysis, AI/ML solution development, document intelligence using OCR and LLMs, and deploying machine learning models with Azure services.

Develops, evaluates, and maintains predictive and statistical models using regression, time-series analysis, and supervised and unsupervised machine learning. Integrates complex aviation, supply chain, and operational datasets; assesses data quality; identifies trends; and documents analytical processes and models. The role collaborates with stakeholders to translate requirements into data science solutions and supports repeatable data, model, and quality-management practices.

Build and deploy machine learning models and end-to-end pipelines for marketing targeting, customer outcomes, and loan origination. Responsibilities include feature engineering, experimentation, model evaluation, monitoring, documentation, governance, and translating analytical findings into business recommendations. The role collaborates with marketing, analytics, product, and cross-functional stakeholders to deliver explainable, compliant solutions.

Develop predictive models and data science solutions for automated credit-risk and lending decisions. Responsibilities include collecting, cleaning, visualizing, and analyzing large datasets; identifying patterns; presenting insights to stakeholders; translating models into business strategies; partnering with IT on implementation; and monitoring model performance.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Transforms hardware quality data into strategic insights through statistical analysis, correlation studies, predictive modeling, automated data pipelines, monitoring systems, and data visualizations. Connects quality metrics across the product lifecycle, identifies root causes and data gaps, and develops automation solutions. Partners with quality, engineering, operations, and executive teams to improve workflows and support strategic decisions through clear, persuasive data storytelling and presentations.