Engineering Manager (Data Science/ML)

About Agero

Wherever drivers go, we’re leading the way. Agero’s mission is to rethink the vehicle ownership experience through a powerful combination of passionate people and data-driven technology, strengthening our clients’ relationships with their customers. As the #1 B2B, white-label provider of digital driver assistance services, we’re pushing the industry in a new direction, taking manual processes, and redefining them as digital, transparent, and connected. This includes: an industry-leading dispatch management platform powered by Swoop; comprehensive accident management services; knowledgeable consumer affairs and connected vehicle capabilities; and a growing marketplace of services, discounts and support enabled by a robust partner ecosystem. The company has over 150 million vehicle coverage points in partnership with leading automobile manufacturers, insurance carriers and many others. Managing one of the largest national networks of service providers, Agero responds to approximately 12 million service events annually. Agero, a member company of The Cross Country Group, is headquartered in Medford, Mass., with operations throughout North America.

Role Description and Mission

The Data Science / ML Engineering Manager is a critical leadership role responsible for managing a team of Data Scientists, ML Engineers, and Software Engineers focused on architecting, building, and operating the next-generation Dispatch Optimization platform. This role demands deep expertise in Data Science, Machine Learning, constrained Optimization (Operations Research), and scalable cloud-native service development. You will drive scientific rigor and engineering excellence to transform model outputs into real-time, high-impact dispatch decisions that directly optimize cost efficiency and service levels.

Key Outcomes

Team Development & People Management

• Leadership & Mentorship: 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.
• Talent Strategy: 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.
• Road-mapping & Project Management: 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.

Strategic Technical Direction & Delivery

• Scientific Strategy: Lead the process to define and select the optimal Data Science, Machine Learning, and Optimization strategy. This includes facilitating deep technical discussions within the team, reviewing and challenging proposed approaches, and making the strategic decision that balances scientific rigor (as assessed by the team), technical feasibility, and overall business impact.
• System Architecture & MLOps: 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.
• Operationalize: 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.
• Technical Excellence: 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.

Communication & Operational Rigor

• Communication: 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.
• Continuous Improvement: 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.
• Operational Compliance: Maintain rigorous operational standards, manage platform development and deployment costs, and ensure security and regulatory compliance activities, including external audits and system documentation.

Skills, Education and Experience

Education

Bachelor's Degree (Master's preferred) in Computer Science, Computer Engineering, Data Science, Operations Research, or a closely related quantitative field.

Experience

• 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.

Role Based Competencies (Knowledge, Skills & Abilities)

• Technical Expertise: 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).
• Leadership and Team Management: Proven ability to inspire, lead, mentor, and hire specialized DS/ML talent, fostering a collaborative, data-driven environment.
• Project Management: Expertise in project estimation, planning, and risk management within an Agile/Scrum framework, including defining and driving technical roadmaps.
• Communication: Exceptional ability to partner with cross-functional stakeholders (Product, Ops) and present scientific and operational findings to executive audiences.
• Innovation and Trend Awareness: 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.

Working Relationships

Collaborates with cross-functional teams, including engineering, product management, QA, and operations. Communicates regularly with senior leadership and external stakeholders as needed.

Additional Requirements

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.