Engineering Manager (Data Science/ML)

Agero, Inc. is a leading provider of digital driver assistance services, focused on enhancing the vehicle ownership experience through data-driven technology. The Engineering Manager for Data Science/ML will lead a team in architecting and operating a Dispatch Optimization platform, ensuring high-quality project delivery and strategic technical direction.

Responsibilities:

• Directly manage and foster a small and high-impact squad of Data Scientists, ML Engineers, and Optimization Specialists, providing expert technical guidance, mentorship, and day-to-day support
• Attract, develop, and retain top talent in modeling, ML engineering, and cloud-native service development by cultivating a positive, inclusive, and collaborative high-performance team culture
• Own the successful implementation and delivery of projects defined on the ML roadmap with the highest quality and on time
• Define and implement robust Software Development Lifecycle (SDLC) processes tailored for ML and optimization (Agile/Scrum)
• Plan and manage platform feature development, including project estimation, risk management, and resource allocation
• Lead the process to define and select the optimal Data Science, Machine Learning, and Optimization strategy
• Guide the design and implementation of end-to-end cloud-native Python services (batch/streaming) that execute constrained optimization algorithms and deliver low-latency, real-time dispatch decisions
• Define and foster the MLOps strategy, ensuring the automation of model training, validation, A/B testing/rollout, and production monitoring using tools like SageMaker, Airflow, or similar industry platforms
• Actively manage technical debt and ensure the prompt resolution of critical production issues by maintaining robust monitoring, alerting, and logging systems
• Collaborate with Architecture to guide platform design and identify opportunities to integrate emerging technology trends
• Partner effectively with Product, Operations, and Data Engineering teams
• Clearly communicate complex technical findings, scientific trade-offs, and operational risks to non-technical stakeholders and executive leadership
• Establish metrics for product performance (e.g., NPS / cost telemetry), monitor operational health, identify failure modes, and drive rapid iteration cycles based on empirical data
• Maintain rigorous operational standards, manage platform development and deployment costs, and ensure security and regulatory compliance activities, including external audits and system documentation

Requirements:

• Bachelor's Degree in Computer Science, Computer Engineering, Data Science, Operations Research, or a closely related quantitative field
• 6+ years relevant experience in Data Science, ML Engineering, or Operations Research, with significant experience transitioning research models into production-grade, scalable systems
• 2+ years proven experience in engineering management or a similar technical leadership role, specifically managing Data Science or ML Engineering teams
• Demonstrated track record of successfully leading and shipping complex DS/ML and Optimization projects (e.g., dispatch platforms, real-time decision engines) that delivered measurable business value
• Experience managing and operating 24x7 real-time information systems and/or technical operations
• Deep understanding of Data Science, ML techniques (e.g., XGBoost, PyTorch, Transformers), optimization methods (MIP/Linear/Stochastic), and architectural requirements for low-latency, real-time decision services
• Skilled in Python, SQL, and Cloud (AWS) MLOps and Data pipelines (Airflow, SageMaker, or equivalents)
• Proven ability to inspire, lead, mentor, and hire specialized DS/ML talent, fostering a collaborative, data-driven environment
• Expertise in project estimation, planning, and risk management within an Agile/Scrum framework, including defining and driving technical roadmaps
• Exceptional ability to partner with cross-functional stakeholders (Product, Ops) and present scientific and operational findings to executive audiences
• Proactively identifies, evaluates, and champions emerging Machine Learning models and research paradigms (e.g., LLMs, Generative AI, Causal Inference, Foundation Models) and assesses their direct potential to solve critical business problems or unlock new product capabilities
• Flexibility to adapt to changing priorities and fast-paced environments
• Availability for occasional travel or extended hours as required for project deadlines production incidents and critical issues
• Master's Degree in Computer Science, Computer Engineering, Data Science, Operations Research, or a closely related quantitative field