Principal Data Scientist - Identity and Matching Models

Team: IT

Location: US

Commitment: Full-time

Workplace Type: remote

Accountabilities

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• Ensure model reliability over time: Monitor changing data distributions, identify outdated models and labels, establish refresh processes, and document methodologies for consistent maintenance.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