Top Hybrid Data Science Jobs

Lead development of customized AI solutions for manufacturing clients. Analyze manufacturing data, build predictive models and tools, identify product opportunities, collaborate with customers and internal teams, and present data-driven findings to technical and non-technical stakeholders.

Develop and maintain credit risk models for origination, behavioral risk, underwriting, and portfolio management across Latin America. Own the full modeling lifecycle, including feature engineering, validation, backtesting, monitoring, calibration, and implementation. Analyze large financial datasets using Python and SQL, monitor portfolio and model performance, address data quality and selection bias, and translate analytical findings into credit strategies. Collaborate with Risk, Data, Engineering, Finance, and Operations teams.

Develops statistical, machine learning, deep learning, and NLP solutions for legal and administrative teams. Responsibilities include building regression and classification models, fine-tuning language models, creating RAG pipelines, designing AI workflows, analyzing structured and unstructured data, producing visual reports, and deploying reproducible production systems. The role collaborates with lawyers, business departments, and technical teams to advance the firm’s AI capabilities and deliver actionable insights.

Data Science interns will use exploratory analysis, statistical techniques, modeling, data mining, and experimentation to understand user behavior, develop product metrics, evaluate A/B tests, and identify product opportunities. They will collaborate with product managers, engineers, designers, and researchers, translating analytical insights into product improvements and business impact while thoughtfully using and validating AI-assisted approaches.

Own an end-to-end machine learning project addressing credit, fraud, growth, or product challenges. Responsibilities include data exploration, feature engineering, model training, benchmarking, deployment, monitoring, LLM application, experimentation, and collaboration with engineering, product, and business stakeholders.

Lead data science for a core fintech product area, defining metrics, experimentation strategies, causal analyses, and models. Partner with product, engineering, design, and marketing to guide roadmap decisions. Build AI product evaluations, automated scorers, regression tests, and model monitoring practices. Contribute to company-wide data science standards, AI-enabled workflows, and measurement frameworks while mentoring teammates and communicating recommendations to senior audiences.

Applied Data Scientist focused on incrementality, causal inference, experimentation, Marketing Mix Modeling, and predictive products. The role partners directly with customers to interpret data, diagnose measurement issues, explain statistical results, and provide actionable recommendations. Responsibilities include building diagnostic tools, analyzing model and experiment outcomes, improving methodologies and production capabilities, and collaborating with Product, Engineering, Media Strategy, Customer Success, and senior data scientists.

Lead and grow Elliptic’s data science team while remaining technically hands-on. Own research direction, datasets, machine learning models, governance, hiring, performance management, and cross-functional delivery. Build capabilities for compliance involving autonomous agents, improve reliability and throughput, and guide model validation, monitoring, and remediation. The role requires strong Python and SQL skills, modern cloud data-stack experience, AI fluency, technical leadership, and expertise analyzing large behavioral or transactional datasets.

Leads finance transformation, data science, analytics, forecasting, anomaly detection, automation, and AI initiatives. Partners with finance, technology, and business leaders to improve reporting, financial data quality, forecasting, and decision-making. Develops data science roadmaps, deploys production models, establishes governance and responsible AI standards, measures business impact, and presents findings to executives. Coaches a data science team and manages cross-functional priorities.

Leads UCI schema analysis, integration, compliance, and lifecycle management for Air Force mission-level Command and Control systems. Designs interoperable data flows, improves data quality, supports testing and validation, documents system changes, and collaborates with technical and operational teams. The role also applies data analytics, machine learning, and statistical methods within secure defense environments.