F
Director of Data Science
PresencialTiempo completoData ScienceEnterprise Software
Lead the production engineering organization behind large-scale AI models, overseeing MLOps, machine learning engineering, data engineering, and the full model lifecycle from data pipelines through production monitoring.
Responsabilidades
- Set priorities and review technical trade-offs across MLOps, machine learning engineering, and data engineering.
- Manage functional leads and hold teams accountable for commitments and delivery dates.
- Own production reliability, incident response, and delivery execution.
- Oversee infrastructure for model training, serving, and large-scale data processing, including costs.
- Define, track, and report organizational KPIs.
- Report progress and risks to senior management, product, and commercial teams.
- Partner with applied research to move results into production.
- Hire senior engineers and determine organizational staffing.
- Establish engineering standards for CI/CD, automated testing, observability, and model monitoring.
Requisitos
- 8+ years of engineering experience, including 5+ years leading teams and managers.
- Experience leading projects across multiple groups with interdependent delivery.
- Production experience with time-series forecasting, reinforcement learning, or large-scale optimization.
- Experience setting direction for a machine learning or data engineering organization.
- Strong understanding of production machine learning systems and architecture review.
- Experience managing delivery commitments, team performance metrics, and staffing.
- Hands-on familiarity with Python, PyTorch, Google Cloud Platform, Dask, and Dagster.
- Bachelor’s degree in computer science, engineering, mathematics, or a related field.
- Ability to communicate technical decisions to commercial stakeholders and commercial constraints to engineering teams.
Se valora
- Master’s or PhD in computer science, machine learning, statistics, engineering, or a related field.
- Experience taking a machine learning platform from prototype to business-critical production.
- Experience in airlines, travel, revenue management, or dynamic pricing.
- Familiarity with real-time inference and high-volume data processing.