Top Hybrid Data Science Jobs

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.

Leverage data science, machine learning, statistical analysis, and big-data techniques to solve business problems and support commercial strategy. Develop, deploy, and measure models, algorithms, automation, and analytical solutions. Collaborate with BI and enterprise data teams to improve reporting, visualizations, and data models. Present insights and recommendations to varied audiences while tracking business impact and evaluating emerging data engineering technologies.

Leads the design and production deployment of data pipelines, data models, analytical applications, and machine learning solutions for aerospace manufacturing. Partners with IT, engineering, manufacturing, cybersecurity, and business stakeholders to deliver reliable, secure, traceable, and scalable analytics. Develops models for anomaly detection, forecasting, optimization, scheduling, and engine performance; establishes technical standards, testing frameworks, documentation, and governance; troubleshoots production issues; and mentors junior team members.

Builds data science and machine learning solutions to develop qualified audiences for marketing campaigns. Responsibilities include data mining, predictive modeling, NLP sentiment analysis, statistical analysis, causal inference, time-series and Bayesian analysis, audience measurement, data integration, visualization, and translating findings for nontechnical stakeholders. The role also supports product innovation, testing, release management, and cross-functional collaboration with agency and product teams.

Develop and deploy statistical, machine learning, forecasting, and optimization models for logistics and warehouse operations. Analyze large datasets from warehouse, transportation, ERP, and operational systems using Python and SQL. Build production-grade analytical solutions, validate and monitor model performance, and communicate insights to technical and non-technical stakeholders. Apply data science to routing, network and warehouse optimization, demand forecasting, anomaly detection, capacity planning, and segmentation.

Consults with business stakeholders to identify problems, gather and prepare data, perform statistical analysis, build models and forecasts, and communicate findings. Develops data-driven recommendations, reusable data assets, dashboards, documentation, and reporting processes. Manages multiple projects, supports process improvements, ensures data quality, and provides peer coaching.

Build and maintain predictive models throughout their lifecycle, identify data patterns and business insights, develop analytic procedures and automation, perform complex ad hoc analyses, monitor model recalibration, and review model scoring and data transformation quality controls.

Ten-week technology internship in Chicago focused on data science and analytics. Interns will transform data into actionable insights and contribute to data engineering, statistical modeling, machine learning, AI solutions, data pipelines, business intelligence dashboards, and data-driven research while developing skills in data architecture, governance, and data quality.

Lead end-to-end dynamic pricing models using demand modeling, causal inference, market simulation, and optimization. Develop pricing approaches for elasticity, seasonality, pacing, and lead-time dynamics; design experiments measuring marketplace effects; and translate complex business problems into deployed solutions. Own production Python code, model deployment, monitoring, retraining, reproducibility, and ML operations while influencing product strategy and collaborating with technical and executive stakeholders.