About the company EXOS is a leader in the field of human performance, a category it created more than 20 years ago. For two decades we’ve stood side by side with the very best, coaching individuals, teams, and communities to higher levels of performance. And we’ve done it all by pioneering and leveraging sports science and proprietary tools across mindset, nutrition, movement, and recovery.
Today, EXOS employs more than 4,000 people in over 400 locations worldwide. With award-winning facilities, sports medicine clinics, technology, and services, EXOS connects people to the solutions they need. We provide comprehensive game plans, regardless of skill and sport, based on time-tested fundamentals and research in order to help people take control of their health and performance. EXOS is trusted by hundreds of clients including leaders in business, technology, health, community organizations, and world champions in sports. Now we’re leaning into our rich heritage to move the brand forward. In 2020, EXOS founder and president Mark Verstegen brought on industry vet Sarah Robb O’Hagan as CEO to lead the company into its next chapter.
Culturally, we promote and reward humility, one team and tenacity as some of our core values and we are committed to cultivating an inclusive work environment where all of our team members feel empowered to grow and bring their whole selves every day. We celebrate the diverse voices and perspectives of our team members, knowing that diversity of age, race, ethnicity, gender identity, sexual orientation, veteran status and national origin, among other social identities, drive innovation, belonging and fuel our work.
Job description
Design, deploy, and support large-scale batch and event-driven ML pipelines with data processing frameworks like Spark and AWS managed services.
Past experience building and embedding ML data pipelines in product, with the goal of delivering business value.
Own the buildout of EXOS’ first ever machine learning infrastructure and overall capabilities
Use best practices in continuous integration and delivery.
Help drive optimization, testing and tooling to improve data quality and our ability to use data to enhance users’ product experience.
Productionize and support the deployment of statistical and advanced machine learning models initially developed by members of the data science vertical, providing consultation and guidance along the way
Built and support machine learning training and inference pipelines to support rapid model development
Collaborate with data engineers, data scientists, software engineers, and other stakeholders, taking learning and leadership opportunities that will arise every single day.
Collaborate with the data analytics vertical to support their BI tools and initiatives to deliver ML-derived data they’ll love working with.
Work in multi-functional agile teams to continuously experiment, iterate and deliver on new product and infrastructure objectives.
Ensure data structures, reports and any other data-related item meets standards and guidelines outlined by EXOS Data Governance..
Work within multidisciplinary teams to identify client needs, define critical success indicators, identify and maintain workflows that fuel machine learning efforts
Keep abreast of new methods and technologies to develop more powerful ML infrastructure.
Be a trusted technical advisor to customers and solve complex ML-related challenges.
Inspire and lead others with your work ethic, business results, intrapersonal skills and willingness to see success based on team accomplishments vs. your individual achievements.
This role will report to the Head of Machine Learning & Data
KNOWLEDGE, SKILLS, AND ABILITIES
Experience as a ML or “full-stack” data science leader for consumer-facing mobile and/or web apps.
Experience creating scalable machine learning systems, particularly infrastructure allowing rapid model re-training, testing, and rapid deployment.
Leveraging a collection of AWS managed services (Sagemaker, Glue, Lambda, S3, Kinesis, etc.), you’ll have experience building the infrastructure required for an end-to-end ML platform, including optimal ETL of data from a wide variety of data sources.
Know how to write distributed, high-volume services in Python and/or Spark, leveraging AWS managed services.
Know how to work with high volume heterogeneous data, preferably with distributed systems on the AWS platform.
An appetite for and prior experience with rapid experimentation.
Knowledgeable about a wide array of data modeling, data access, and data storage techniques.
Appreciate agile software processes, data-driven development, reliability, and responsible experimentation.
Want to own the code you write in production.
Understand the value of partnership within and across teams.
Care a lot about fostering a diverse culture that includes everyone and supports them being their authentic self. We strongly believe that diversity of experience, perspectives, and background will lead to a better environment for our employees and a better product for our customers.
A successful history of manipulating, processing and extracting value from large disconnected datasets.
Superior understanding of database query languages and substantial knowledge in analytical approaches.
Expertise with data mining and visualization techniques, ability to apply context to data, and strong ability to communicate the data (verbal and written).
REQUIRED QUALIFICATIONS
Bachelor’s degree in a technology-related field (data science, computer science, software engineering, etc.), with 5+ years of prior ML engineering, data engineering, data science, and/or software development experience.
Proven experience as an ML engineer leader
Experience with data structures, common ML algorithms, and software architecture
Experience in writing software in one or more languages: Python, Spark, Java, Go, Typescript, etc. Vast familiarity with SQL.
Experience managing client-facing projects, troubleshooting technical issues, working with cross-functional stakeholders.
Experience in working with/on data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures.
Excellent verbal and written communication skills.
PREFERRED QUALIFICATIONS
Hands-on experience designing, deploying, and supporting machine learning embedded within a D2C product
Hands-on experience in big data, information retrieval, data mining or machine learning.
Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
Familiarity with MLOps and related design principles
Experience architecting, developing software, or large-scale data solutions in cloud environments.
Reflective, independent and eager learner (e.g., learns from mistakes, asks good questions, able to generate creative solutions to problems with minimal guidance).
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