- 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.
Requirements
- 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, BigQuery) 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 (SageMaker, 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.
Core Competencies
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.
Highest-signal resume keywords
- Production ML Model Development
- Data Pipeline Management
- A/B Testing and Experiment Design
- Python and SQL Proficiency
- MLOps Platform Contribution
ATS Optimization Keywords
Hard Skills
- Machine Learning
- Data Engineering
- Statistics
- Experiment Design
- Python
- SQL
- ML Frameworks
- Data Warehousing
- Real-Time ML Systems
- Software Engineering
Soft Skills
- Excellent Communication
Industry Keywords
- Fintech
- Financial Services
- Marketplace
- Auction Environments
Tools & Technologies
- Redshift
- Snowflake
- Dbt
- Airflow
- SageMaker
- Spark
- Ray
- MLflow
- Docker
- Kubernetes