Top Data Science Jobs in Columbus, OH

Lead development and operationalization of measurement products (MMM, incrementality, insights, recommendations). Build, deploy, and maintain production-grade statistical and ML systems, debug end-to-end data and model issues, evaluate methodologies, and partner with engineering, product, and customer teams to improve measurement accuracy and reliability. Provide customer-facing explanations and support for measurement outputs.

Build and deploy credit and fraud risk models using advanced ML/statistical methods. Partner with stakeholders, extract insights from large transaction datasets, automate feature/production pipelines, mentor junior data scientists, and ensure model quality and deployment scalability.

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 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.