Top Hybrid Data Science Jobs

The Senior Data Scientist will lead the development of personalization algorithms, collaborate cross-functionally, build data pipelines, and optimize machine learning models.

As the Senior Data Scientist for AI & Model Risk, you will lead AI risk assessments, coordinate with stakeholders, evaluate AI system designs, and ensure compliance with risk management principles in financial services.

As a Data Science Intern, you will develop and validate LLM pipelines for extracting clinical endpoints from EHR data, ensuring data quality for clinical analysis.

Partner with Marketing to own attribution, ROI, conversion, and funnel analysis. Design experiments, surface proactive insights, optimize marketing spend, and collaborate with Data/Analytics Engineers to build data models and pipelines. Communicate findings and recommendations to non-technical stakeholders.

The Data Scientist will analyze data to provide insights, partner with teams for metrics, develop models, and create accessible visualizations to influence product and program strategies.

Work with the Data Science team to extract behavioral signals from call transcripts, validate predictive models, clean and analyze large datasets, experiment with LLM/GenAI workflows, and communicate findings through charts, notebooks, and write-ups to inform business decisions.

As a Senior Data Scientist, you will develop predictive models, conduct EDA, collaborate cross-functionally, and drive data-driven decisions.

As a Staff Data Scientist, you will lead complex marketplace problem-solving, architect ML systems, mentor teams, and drive data science strategy in partnership with engineering and product management.

Maintain and audit high-resolution image datasets, correct labels and classifications, support data annotation quality, diagnose and validate deep learning model issues, perform field tests, document results, and collaborate with support and field teams to prioritize fixes that improve product reliability and customer satisfaction.

Lead growth-focused data science initiatives, owning LTV prediction models across countries and channels. Translate business questions into models, analyze large customer and marketing datasets, evaluate acquisition quality, monitor and productionise models with engineering partners, and drive data-informed growth and retention decisions.