• Own our Data Science platform and our 1-2 year roadmap for creating a sophisticated and stable platform • Embed into science projects so engineering quality is built in from day one • Create the templates to get new systems up and running quickly • Push our AI‑assisted development agenda • Be a thought leader for the Veho data and engineering community on how to leverage AI in improving development velocity • Manage the Machine Learning Operations team • Write code, review designs, and set the technical bar • Partner closely with our Agentic Developer Experience and Builder Experience teams

• Lead and grow a team of six data scientists / applied ML engineers building production models across route success, last mile route building, and forecasting • Deeply understand the highest-leverage problems, partner with your team to choose the ML / OR methodologies, and drive models from the first prototype through deployment, monitoring, and iteration • Ship and maintain production systems. You'll personally build, deploy, and maintain models in production. You stay in the codebase, review your team's PRs, and debug a failing model or a broken pipeline yourself when needed • Partner closely with the ML Platform / ML Operations team so models deploy on stable infrastructure, and push modeling requirements back into the platform so the next project is faster • Drive AI usage across the modeling workflow. Set standards, introduce patterns, and drive adoption of how to leverage AI in data science work (EDA, feature and model iteration, ML methodologies) • Be part of the on-call rotation for our data science production systems

• Build reliable, efficient, and scalable infrastructure for our AI/ML capabilities • Create robust data pipelines to feed analyses and models • Enable forecasting, network orchestration, and live pricing systems • Ensure data quality and data integrity through best practices in data integration • Build out robust feature stores, model orchestration tooling, experimentation tooling, model performance monitoring • Create standards and templates for model development and deployment across all Data Science teams

• Partner with PhD researchers to design, implement, and productize machine learning models that drive quantitative trading strategies • Develop and maintain complex data pipelines, including data ingestion, feature engineering, validation, and quality monitoring • Translate research prototypes and novel ideas into performant, well-tested, production-ready code • Build extensible tools and frameworks that accelerate the model development and experimentation lifecycle • Supervise, understand, and remediate subtle data quality issues across both research and production environments • Proactively lead projects from requirements through delivery, making autonomous decisions about scope, dependencies, and trade-offs, with an emphasis on long-term maintainability • Coordinate and contribute to deployment efforts while guiding junior engineers and researchers; align with research and engineering stakeholders on ownership, execution, and prioritization • Foster engineering consistency, standards, and best practices within Research