About the job
About Agero:
At Agero, we are at the forefront of transforming the vehicle ownership experience. Our mission is to innovate and enhance this experience through a unique blend of passionate individuals and data-driven technology, which strengthens the bonds between our clients and their customers. As the leading B2B, white-label provider of digital driver assistance services, we are redefining the industry by turning manual processes into digital, transparent, and connected solutions. Our offerings include an industry-leading dispatch management platform powered by Swoop, comprehensive accident management services, expert consumer affairs, and connected vehicle capabilities, along with a growing marketplace of services, discounts, and support powered by a strong partner ecosystem. We are proud to cover over 150 million vehicles in collaboration with major automobile manufacturers, insurance carriers, and more. Managing one of the largest national networks of service providers, Agero handles approximately 12 million service events annually. Headquartered in Medford, Mass., with operations across North America, we are a proud member of The Cross Country Group. To learn more, visit https://www.agero.com/.
Note: For our technical roles, we encourage an in-person start! You may need to travel to Medford for your initial onboarding. Don't worry about the logistics; once you’re hired, we take care of all travel arrangements and expenses.
Role Description and Mission:
As the Engineering Manager for Data Science and Machine Learning, you will play a pivotal leadership role overseeing a talented team of Data Scientists, ML Engineers, and Software Engineers. Your focus will be on architecting, building, and operating our next-generation Dispatch Optimization platform. This position requires profound expertise in Data Science, Machine Learning, constrained Optimization (Operations Research), and the development of scalable cloud-native services.
You will lead scientific rigor and engineering excellence to convert model outputs into real-time, high-impact dispatch decisions that optimize cost efficiency and enhance service levels.
