Top Hybrid Data Science Jobs

Build and deploy predictive models, recommendation systems, LLM applications, incentive optimization frameworks, and causal inference models for customer growth. Partner cross-functionally to improve acquisition, engagement, retention, and expansion. Measure marketing impact, guide investment decisions, develop AI analytics and decision systems, and provide technical leadership and mentorship across data science initiatives.

Leads a 12-person data science and machine learning organization, setting strategy for predictive modeling, causal inference, AI evaluation, and ML infrastructure. Partners with product, engineering, and business leaders to apply data science to growth, retention, product optimization, support allocation, and risk modeling. Builds team structure, processes, technical standards, and culture while managing managers and senior technical contributors.

Leads applied machine learning and statistical research for health features using longitudinal wearable, physiological, behavioral, and multimodal data. Designs studies, models, evaluation protocols, and quality standards; partners with engineers to productionize methods; advises product, clinical, regulatory, and digital health teams; mentors scientists and engineers; and communicates evidence, limitations, risks, and trade-offs to technical and executive stakeholders.

Leads Mastercard’s Open Finance data science strategy and global organization. Oversees production-grade AI and machine learning solutions for transaction categorization, entity extraction, fraud detection, credit risk, propensity modeling, personalization, and financial insights. Partners with Product, Engineering, Sales, Risk, Marketing, Legal, and Compliance to commercialize scalable solutions, establish MLOps and governance practices, ensure responsible AI, and grow high-performing data science teams.

Lead research, development, deployment, and measurement of machine learning models for forecasting, pricing, inventory optimization, personalization, and generative AI. Build production-grade software and scalable machine learning workflows, collaborate with engineering and business teams, communicate insights to stakeholders, and support enterprise data science applications across testing and production environments.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and operate uplift and machine learning models that optimize personalized retention bonuses. Responsibilities include feature pipeline development, model deployment, monitoring, experimentation, causal analysis, large-scale behavioral data analysis, and communicating business impact to Marketing stakeholders. The role also involves weekly model operations, cross-functional collaboration, documentation, code review, and developing reusable modeling practices.

Participate in a paid, project-based summer internship at the Federal Reserve Bank of Chicago. Interns work 40 hours weekly for 10–12 weeks, contribute to departmental assignments, interpret data and analytics, develop insights, support decision-making, present information, and collaborate with internal and external stakeholders in an onsite or hybrid environment.

Join Celonis’s 12-month Orbit Program as an Associate Value Engineer. Consult customers, identify business challenges, build AI and machine-learning prototypes, develop SQL and PQL data models, lead proof-of-concept projects, present solutions to executives, quantify ROI, and establish trusted customer relationships. The role combines technical analytics, enterprise software consulting, customer engagement, and value realization, with mentorship and training provided.

Lead end-to-end data science and machine learning initiatives, from identifying business use cases and querying data through prototyping, evaluation, deployment, and monitoring. Partner with Product, Engineering, and Machine Learning Engineering teams across areas such as risk, insurance, and payments. Develop predictive models, statistical methods, deep learning, and LLM solutions while contributing reusable data assets, tooling, MLOps strategy, technical reviews, and data-informed business decisions.