Senior Data Scientist

CLEAR - Corporate•New York, NY

2d•$175,000 - $215,000•Onsite

About The Position

Have you ever had that green-light feeling? When you hit every green light and the day just feels like magic. CLEAR's mission is to create frictionless experiences where every day has that feeling. With more than 30+ million passionate members and hundreds of partners around the world, CLEAR’s identity platform is transforming the way people live, work, and travel. Whether it’s at the airport, stadium, or right on your phone, CLEAR connects you to the things that make you, you - unlocking easier, more secure, and more seamless experiences - making them all feel like magic. As CLEAR continues to scale, we’re deepening our investment in the data science ecosystem that powers our products, personalization, and decision-making. We’re looking for a Senior Data Scientist to design, develop, and deploy advanced statistical and machine learning models that drive measurable business impact on our digital identity platform. This is a hands-on senior individual contributor role for someone who blends strong modeling expertise, exceptional Python engineering craft, familiarity with modern ML tooling, and a passion for translating complex data into clear, actionable intelligence. You’ll build and operationalize models that help CLEAR understand, predict, and optimize behavior across our digital identity platform delivering insights and automation that scale.

Requirements

6+ years of experience in data science, machine learning, or applied statistics within a modern cloud data environment like AWS Sagemaker.
Expert-level Python with deep experience in scientific libraries (e.g. pandas, NumPy, SciPy, matplotlib), machine learning frameworks (e.g. scikit-learn, XGBoost, LightGBM, pytorch), and model evaluation tooling.
Strong SQL skills and experience working with cloud data warehouses (e.g. Snowflake, BigQuery, Redshift).
Experience with modern ML workflow tools (e.g., Airflow, Dagster, MLflow, Vertex / SageMaker, Voxel 51).
Deep understanding of statistical methods, experiment design, data cleansing, feature engineering, and model interpretability.
Proven ability to turn ambiguous business problems into measurable modeling approaches—balancing analytical rigor with practical business impact.
Experience deploying production models and maintaining them through their lifecycle (monitoring, retraining, performance management).
Strong communication, stakeholder influence, and storytelling skills; you can translate complex data science work into insights and decisions.
A proactive, curious mindset with a passion for standardization, repeatability, and scaling high-quality modeling practices.

Responsibilities

Own and evolve CLEAR’s predictive modeling ecosystem: design, train, and optimize statistical and machine learning models for our digital identity platform that support product, risk, fraud, operations, and member experience teams.
Develop high-quality production code including feature engineering pipelines, model training workflows, and evaluation frameworks that meet reliability and performance standards.
Partner with Data Engineering and ML Platform teams to deploy models into production, define scalable data pipelines, and ensure real-time and batch inference systems run efficiently.
Advance CLEAR’s AI & ML capabilities by designing reusable modeling components, improving model documentation, and contributing to a roadmap for integrating ML into core products and decision flows.
Improve experimentation and insight generation by building robust tooling and analytical frameworks, designing statistical tests, and synthesizing results into clear and actionable recommendations for cross-functional teams.
Mentor analysts, data scientists, and ML engineers, guiding them on Python best practices, model development, data exploration, and communicating insights effectively.