Top Hybrid Data Science Jobs

Analyze digital payment adoption, engagement, transaction flows, and member behavior using large datasets. Build statistical and predictive models, dashboards, and visualizations with Python, SQL, Databricks, Power BI, or Tableau. Translate analytical findings into recommendations for product strategy, member experience, operational efficiency, economics, and risk, while presenting insights to technical and business stakeholders.

Conduct quantitative analysis on large datasets to support predictive and machine-learning model development and validation. Identify data errors, explore patterns and anomalies, answer business questions, create visualizations, and perform basic diagnostic and model suitability testing under supervision. The role also develops proficiency in statistical methods, data wrangling, analytical programming, and model-building concepts.

Build and deploy scalable healthcare actuarial models within production software and AI systems. The role involves actuarial governance, model validation, data pipeline integration, AI evaluation, rapid prototyping, and technical collaboration with Product, Engineering, and Delivery teams. Candidates must translate complex healthcare risk and claims data problems into reproducible software solutions using Python, R, and SQL.

Analyze product and customer data using Python, SQL, statistical methods, and data science tools to identify patterns and inform product decisions. Support CliftonStrengths product analytics, collaborate with data scientists and product managers, communicate findings to stakeholders, and contribute data-driven insights to marketing, targeting, and growth initiatives. The internship also involves exploring modern AI tools and working onsite in San Francisco Monday through Thursday.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Develop, deploy, and monitor predictive models and algorithmic decision systems for automated sports pricing and market making. Prepare data, build backtesting and simulation tools, analyze model and trading performance, and improve quoting, pricing, exposure management, and execution quality. Create dashboards and diagnostics while collaborating with traders, quantitative engineers, product, and engineering teams. The role includes occasional evenings, weekends, and holidays aligned with major sporting events.

Leads the design, development, deployment, and monitoring of advanced statistical and machine learning models for insurance pricing, underwriting, claims, fraud, retention, and growth. Advises executives and actuarial teams, establishes modeling and experimentation standards, ensures responsible and scalable productionization, and mentors data scientists. The role also advances MLOps, AI infrastructure, model lifecycle management, explainability, fairness, and data science strategy.

Partner with Product, Engineering, and Go-To-Market teams to define success metrics, design experiments, evaluate product launches, analyze large-scale ad-tech data, and guide roadmap decisions. The role requires translating complex data science findings into actionable recommendations for technical and non-technical stakeholders while applying rigorous statistical, modeling, and experimentation practices.

Supports the AI, Data and Digital Transformation team through the data science lifecycle, including analyzing large datasets, identifying trends, developing and evaluating predictive and machine learning models, and presenting project findings and business impact. The intern will contribute to analytics and digital transformation initiatives, collaborate with team members, and gain project management, teamwork, and leadership exposure.

Lead end-to-end AI and data science initiatives across Alcoa’s Operations and Enterprise functions. Develop, validate, deploy, and continuously improve statistical, machine learning, generative AI, RAG, and agentic AI solutions. Translate business challenges into scalable models, evaluate outcomes, and deliver measurable value. Provide technical leadership, mentor data scientists, and collaborate with engineers, architects, analysts, and stakeholders to build trusted, responsible AI capabilities.