Top Hybrid Data Science Jobs

The Senior Data Scientist will analyze data to support product features, conduct quantitative research, and collaborate with cross-functional teams to enhance creator monetization efforts.

As a Senior Data Scientist at Haystack News, you will utilize statistical analysis and machine learning to enhance user engagement and retention, impacting product success.

The Director of Data Strategy will develop and execute a data strategy, ensuring data quality and governance across the organization, fostering collaboration among various departments for improved analytics and business outcomes.

Develop and scale AI-powered features for Vendelux's platform, focusing on data-driven solutions and enhancing user experience in event marketing.

As a Senior Business Intelligence Analyst, you will leverage data to provide insights, improve field performance, and support strategic decision-making.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

Lead development of automated, auditable revenue data pipelines and anomaly detection to improve reconciliation and data integrity. Build reusable financial data assets, dashboards, and reports; collaborate with finance and cross-functional teams to support month/quarter-end close and audit readiness.

Design and analyze experiments and causal studies, build retention measurement and predictive models, develop scalable PySpark/SQL data pipelines, create dashboards, and partner with product and growth teams to improve user engagement and retention.

Lead data science for a new 0->1 B2B product: define strategy, frame high-impact questions, run experiments, design metrics/telemetry and dashboards, align global analytics, and translate insights into product decisions.

Lead the fraud data science function: define ML strategy, build and productionize fraud detection models, investigate fraud patterns, run experiments, collaborate with Product/Engineering/Risk, and deliver dashboards and monitoring to minimize fraud losses while preserving customer experience.