Top Software Data Science Jobs

As a Senior Data Scientist, you'll develop learning systems, algorithms, and models for optimizing marketing campaigns while collaborating with product development teams and engineering.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

As a Senior Data Scientist, you'll lead projects leveraging data to enhance healthcare outcomes, mentor team members, and align data efforts with business goals.

The Data Scientist will analyze geospatial data, communicate insights, collaborate with teams, and enhance data processes to support national security missions.

Lead design and execution of geo holdouts and incrementality tests, build DMA-level LTV models and geo-tiered bidding, define objective functions and decision rules for automated bidding, set experiment power and success criteria, and support paid and organic growth teams.

Lead and grow an applied/data science team to define vision, develop ML and causal inference solutions, establish experimentation and measurement best practices, translate business problems into scalable models, partner with Product and Engineering to productionize models, and influence executive strategy with data-driven insights.

Provide analytical support in Data Science Division, design analytics solutions, and develop end-to-end analytical lifecycles. Collaborate with diverse teams and present findings to technical and non-technical audiences.

Momenti seeks a Data Scientist to analyze user gesture data and improve content interaction. Responsibilities include developing data pipelines and visualizing user behavior insights.

Create and review realistic, locale-specific mathematics tasks and scoring rubrics in the target language to evaluate AI systems. Score submissions for accuracy and regulatory alignment, produce gold-standard solutions, and provide expert feedback to improve AI performance in local mathematical contexts.

Lead end-to-end data science initiatives across pricing, dispatch, ETA, routing, and forecasting. Turn ambiguous logistics problems into production ML solutions, build data pipelines, partner with engineering to deploy models, establish evaluation and A/B testing, and work directly with customers and leadership to measure business impact.