Data Scientist

• Own and develop production ML models for real-time recommendations, pricing, and conversion prediction across the Engine marketplace.
• Design, build, and manage feature pipelines and data transformations in our data warehouse (e.g., Redshift, Snowflake) using tools like dbt, Airflow, and SQL to ensure high-quality, timely features for model training and serving.
• Lead the design, execution, and analysis of large-scale A/B tests and experiments, translating results into actionable product and model improvements.
• Collaborate closely with product managers, partner managers, and business stakeholders to translate complex business problems into well-scoped data science projects.
• Work hand-in-hand with engineering teams to deploy, monitor, and maintain ML models in production—including real-time serving infrastructure.
• Drive best practices across the team in model development, code quality, documentation, experiment design, and reproducibility.
• Contribute to the evolution of our MLOps platform and tooling, ensuring scalable and reliable model lifecycle management.
• Present findings, model insights, and strategic recommendations to executive and non-technical stakeholders with clarity and business context.

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Physics, Economics, or a related quantitative field (or equivalent professional experience).
• 7+ years of experience across data science, machine learning, and data engineering.
• Designing and shipping production ML models and advanced analytics in applied, production-oriented settings using Python, SQL, and ML frameworks.
• Building real-time or near-real-time ML systems for recommendations, pricing, bidding, or similar use cases.
• Working with data warehouse technologies (Redshift, Snowflake, Big Query) and building/managing data pipelines (dbt, Airflow, Spark).
• Strong foundation in statistics, probability, experiment design, and machine learning theory.
• Experience working with ML platforms and infrastructure (Sage Maker, Spark, Ray, MLflow, or equivalent).
• Comfortable doing software engineering when needed—writing application code in Python/Scala/Java, contributing to APIs, containerizing services (Docker, Kubernetes), or building CI/CD for model deployments.
• Excellent communication skills—effective with both technical and non-technical audiences.
• Experience in fintech, financial services, or marketplace/auction environments is a strong plus.

Demonstrates expertise in developing and deploying production ML models, managing data pipelines, and conducting A/B testing to drive actionable insights. Proficient in collaborating with cross-functional teams to translate complex business challenges into data-driven solutions.

• Production ML Model Development
• Data Pipeline Management
• A/B Testing and Experiment Design
• Python and SQL Proficiency
• MLOps Platform Contribution

• Machine Learning
• Data Engineering
• Statistics
• Experiment Design
• Python
• SQL
• ML Frameworks
• Data Warehousing
• Real-Time ML Systems
• Software Engineering

• Excellent Communication

• Fintech
• Financial Services
• Marketplace
• Auction Environments

• Redshift
• Snowflake
• Dbt
• Airflow
• Sage Maker
• Spark
• Ray
• MLflow
• Docker
• Kubernetes