Top Remote Data Science Jobs in Baltimore, MD

Proactively explore RevenueCat's data to identify product opportunities, translate ambiguous problems into analyses and production-grade predictive/descriptive models, partner with Product and Engineering to ship data-powered features (LTV prediction, experimentation, benchmarking), define statistical approaches, iterate on models in production, and communicate insights to technical and non-technical audiences.

The Senior Data Scientist will integrate data from various sources, analyze web performance, and provide insights for strategic decision-making. Responsibilities include developing automated systems for content coverage, tracking metrics, and designing experiments to assess new initiatives' impact.

The Principal Data Scientist will lead analytics for Mortgage sales, focusing on customer retention and lead optimization. Responsibilities include applying advanced analytics to enhance marketing strategies, conduct customer segmentation, and oversee lead generation processes. This role requires strong programming skills and collaboration with business partners to communicate insights effectively.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.

The Senior Data Scientist oversees data collection and preparation, designs study protocols, leads report development, trains junior employees, and manages project workstreams while collaborating with team members to optimize business performance.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.