Principal Data Scientist - Identity and Matching Models

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Data Scientist – Identity and Matching Models based in the United States.

As a Principal Data Scientist, you will lead the development of advanced identity resolution and record-matching models that help people and businesses make more accurate, confident connections. You will own the full modeling lifecycle, from research and experimentation to production deployment, performance monitoring, and continuous improvement. Working with large, complex datasets from multiple sources, you will develop reliable solutions even when ground-truth labels are limited or unavailable. You will also shape evaluation methods, establish meaningful success metrics, and ensure model quality through rigorous testing and data validation. In this remote-first environment, you will collaborate closely with product, engineering, and data science teams while mentoring colleagues and influencing technical priorities. This role is ideal for an experienced data scientist who combines strong machine learning expertise with sound judgment, responsible data practices, and a commitment to measurable product impact.

• Develop identity and record-matching models: Design, train, test, and deploy models that determine whether records refer to the same individual and quantify confidence in matching results.

• Establish robust evaluation frameworks: Develop labeling strategies, build evaluation datasets, conduct manual quality checks, and estimate model accuracy when comprehensive ground-truth data is unavailable.

• Leverage AI-assisted labeling: Use AI models to support large-scale data labeling while validating their outputs against human judgment and maintaining reliable evaluation standards.

• Deploy and maintain production models: Collaborate with machine learning engineering and DevOps teams to deploy models on AWS and maintain retraining schedules, model registries, version tracking, and data lineage.

• Monitor data and model quality: Review data pipelines and model outputs before release, identify changes and regressions, investigate performance shifts, and communicate findings and limitations to technical and business stakeholders.

• Drive product impact: Partner with product owners to translate analytical findings into clearly defined user stories, measurable targets, and prioritized research initiatives.

• Ensure model reliability over time: Monitor changing data distributions, identify outdated models and labels, establish refresh processes, and document methodologies for consistent maintenance.

• Promote technical excellence: Review code, document research and modeling decisions, share knowledge, and contribute to data science and machine learning working groups.

• Maintain responsible data practices: Ensure models and data workflows comply with applicable opt-out requirements, retention policies, and data lineage standards.

Requirements

• Applied data science experience: At least 6 years of experience in applied data science, with demonstrated results from developing and delivering models into production.

• Educational background: A bachelor's degree in a quantitative discipline is expected, while a master's degree is preferred. Equivalent professional experience may also be considered.

• End-to-end machine learning delivery: Experience taking models from initial research through cloud-based production deployment, monitoring, and post-launch performance evaluation.

• Technical proficiency: Strong Python and SQL skills, along with experience using Spark or similar distributed computing technologies to process very large datasets, including datasets with 100 million or more rows.

• Cloud and ML tooling: Practical experience with AWS SageMaker, Databricks, MLflow, or comparable platforms for model development, deployment, tracking, and monitoring.

• Model evaluation expertise: Ability to assess model quality when reliable ground-truth labels are limited, using sampling, manual review, and statistically informed estimates of accuracy and confidence.

• Analytical judgment: Ability to distinguish meaningful improvements from statistical noise and explain model performance, limitations, uncertainty, and trade-offs clearly.

• Model lifecycle management: Experience monitoring data changes, identifying model or label degradation, maintaining retraining processes, and documenting workflows for reproducibility and knowledge sharing.

• Responsible data management: Understanding of how to handle personal data appropriately, including adherence to opt-out rules, data retention requirements, and data lineage practices.

• Communication and collaboration: Strong written and verbal communication skills, with the ability to work effectively with engineers, product owners, and cross-functional stakeholders.

• Preferred expertise: Experience in record linkage, entity resolution, deduplication, graph-based methods, AI-assisted data labeling, or converting raw model scores into actionable confidence measures.

• Location eligibility: Candidates must be based in one of the following eligible US states: Arizona, California, Colorado, Florida, Georgia, Kentucky, Maryland, Michigan, Missouri, New Jersey, Nevada, North Carolina, Pennsylvania, South Carolina, South Dakota, Texas, Virginia, Washington, or Wyoming.

Benefits

• Competitive annual salary: $166,500–$216,000, depending on factors such as experience, skills, job level, and market conditions.

• Annual bonus program: Eligibility to participate in an annual bonus program.

• Equity opportunities: Stock options.

• Retirement savings: 401(k) matching.

• Healthcare coverage: 100% coverage for medical, dental, and vision insurance.

• Flexible paid time off: Unlimited PTO.

• Mental health support: Access to mental health resources.

• Remote work support: Funding for home office equipment.

• Fitness reimbursement: Financial support for eligible fitness expenses.

• Professional development: Assistance with course expenses to support continued learning and career growth.

• Remote-first work environment: Fully remote work for eligible employees based in approved US states.

• Collaborative culture: Opportunities to contribute to technical initiatives, exchange knowledge, and help shape data science practices across a growing team.