About The Role:Veho's Data Science team is core to Veho's ability to deliver millions of packages by creating the systems that drive forecasting, network orchestration, pricing, and routing decisions. Being part of this team, the Machine Learning Operations team drives the foundation of these systems by being a partner to the data scientists to create well-designed, stable, and performant systems.
As the Technical Lead Manager, you'll own our Data Science platform and our 1-2 year roadmap for creating a sophisticated and stable platform that keeps up with Veho's rapid growth. You and your team embed into science projects so engineering quality is built in from day one, and create the templates to get new systems up and running quickly. You'll push our AI-assisted development agenda and be a thought leader for the Veho data and engineering community on how to leverage AI in improving development velocity.
You will manage the Machine Learning Operations team and contribute significantly by writing code, reviewing designs, and setting the technical bar. You'll partner closely with our Agentic Developer Experience and Builder Experience teams.
A great candidate: - Is an expert in their craft, creating high quality ML infrastructure and delivering impactful machine learning models to our stakeholders.
- Works in close collaboration with the other Data Science team members and keeps the business value at the center of their work. Has a bias for action, balancing delivering impact in the short-term while building out the long term vision.
- Applies their ML / MLOPS knowledge to suggest new patterns, tools, approaches to improve the team's models
- Drives team velocity by helping the current team develop in their careers, hiring strong new talent onto the team, and adopting AI as a core part of development.
What you'll do:- Lead and grow a team of four engineers spanning ML infrastructure, ML operations, and embedded data science project work.
- Improve our internal ML platform: standardize and improve ML infrastructure, improve how DS services are created, deployed, and operated. Think service performance, permissioning, environment setup, and integration with upstream and downstream systems.
- Set the roadmap for improving our Machine Learning and Operations Research infrastructure.
- Embed engineers into major science initiatives (forecasting, network orchestration, pricing) so every project is technically sound and lessons learned find their way back into our platform.
- Drive AI usage across DS. Collaborate with our Agentic Developer Experience team to ensure new tooling has a high impact on the Data Science team's velocity. Set standards, introduce patterns, and drive adoption of how to leverage AI in data science workflows (EDA, model iteration, ML/OR methodologies)
- Be part of the on-call rotation for our data science production systems.
What You Bring:- Bachelor's Degree plus at least 6 years of experience in Machine Learning Engineering, or Master's Degree plus at least 4 years in Machine Learning Engineering:
- This experience should include:ML platform experience: training and serving infrastructure, feature stores, orchestration, monitoring, deployment pipelinesexperience managing impactful, high velocity ML Platform / ML Ops teams in smaller scale companiesexperience driving AI/agentic tooling adoption inside an organization
- hands-on experience with open-source tooling for large-scale ML (e.g., Ray, Flink, Feast).
- strong knowledge of Cloud-based data engineering and data science tools (AWS preferred) and Data Warehouses (Redshift, Databricks, Snowflake).
- Strong proficiency in Python.
- Interest in building systems in a Supply Chain setting, enabling a physical supply chain to run like clockwork.
