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# Data Scientist - Identity

- **Company:** LiveRamp
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $130,000.0 - $196,500.0
- **Contract:** Permanent contract
- **Skills:** Big Data, BigQuery, Cloud Computing, Python (Programming Language), Machine Learning, NumPy, SQL Databases, Pandas, Scikit Learn, Information Technology, Data Analytics, Integration Frameworks
- **Published:** October 16, 2026
- **Apply:** https://www.juju.com/job/1_jobscore790-4c9e59e1

## About the Role

About you: + MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related quantitative field, or equivalent practical experience. + 3+ years of experience designing, building, and deploying data science or machine learning solutions in a production environment. + Proficiency in Python and SQL, along with experience using common data and ML libraries and frameworks (for example, pandas, NumPy, scikit-learn, or similar). + Experience working with large datasets in a cloud environment and with modern data processing frameworks or warehouses (for example, BigQuery). + Demonstrated ability to independently frame ambiguous business or product questions as concrete, testable data science problems. + Strong analytical and problem-solving skills, with a focus on clear measurement, experimentation, and data-informed decision-making. + Excellent written and verbal communication skills, including the ability to present complex technical topics
to both technical and non-technical audiences. + A product-focused mindset and a strong bias toward iterative execution-you are comfortable moving from idea to prototype to production quickly while incorporating feedback.

## Description

Your team will: + Build and evolve data science capabilities that sit at the heart of LiveRamp's identity and data collaboration products, working closely with engineering teams across the company. + Tackle a diverse portfolio of problems, from improving core matching and graph-based algorithms to powering customer-facing features for targeting, measurement, and analytics. + Collaborate cross-functionally with product, engineering, customer success, and go-to-market teams to ship solutions that are technically sound, operationally scalable, and aligned with customer needs. + Maintain a culture of experimentation and scientific rigor, using well-designed tests, strong baselines, and clear metrics to guide decisions. + Invest in shared tooling, libraries, and best practices that raise the bar for how data science is done and operationalized across LiveRamp.