Top Senior Data Science Jobs in Richmond, VA

The Senior Data Scientist in Conversational AI & NLU designs and optimizes AI solutions, evaluates models, collaborates with stakeholders, and analyzes conversational data to improve user experience.

Lead the development and deployment of advanced ML/AI products, mentoring teams and collaborating with stakeholders to influence business outcomes.

The Senior Data Scientist at Fetch will develop and scale data science capabilities, architect predictive models, and influence product strategy through collaboration and experimentation. They will lead initiatives focused on engagement, churn, and business impact measurement using advanced analytics and machine learning tools.

The Senior Data Scientist will develop machine learning models, conduct A/B tests, and work with behavioral data to enhance customer engagement and partner ROI.

Lead data science for Yahoo Mail subscriptions: analyze acquisition, conversion, pricing, retention, and churn; design and analyze A/B and multivariate experiments; build predictive and clustering models; partner with product, engineering, design, and business teams to deliver data-driven growth and monetization insights.

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.