This role exists to support the business in utilizing Machine Learning applications to drive additional value for our customers by feeding into the delivery of strategic data science solutions whilst setting up resilient and future-proof ML infrastructure and engineering foundations.

Designing and developing our ML Operations (MLOps) infrastructure and practices to support the effective transition of machine learning models and PoCs into production in a cost and operationally efficient manner.
Monitoring, and maintaining the health of new and existing ML models post deployment; Working with business stakeholders to proactively raise awareness of any risks to the model(s) outputs and/or their interpretation, and usability.
Implementing modern code development and MLOps strategies, in collaboration with Data Science and Data Engineering to support the proactive identification, targeting and resolution of any ML model(s) performance issues across their entire lifecycle.
Developing operating procedures, work instructions and high-level designs of ML pipelines and architectures to support the effective documentation of our work.
Working with third party partners and software providers to improve, implement and/or support the move to production of novel Machine Learning solutions.
Raising awareness of best practices for MLOps; collaborating and knowledge sharing with individuals across the data and software teams, and wider business.
Collaborating with Data Science, Data Engineering and Software Engineering teams to drive cross-function visibility and reduction of technical debt across the teams.
Designing and implementing ML infrastructure to support the deployment and monitoring of ML models in production in an efficient and cost-effective manner.
Developing an MLOps strategy aligned to best practice and existing DevOps practices in Gymshark.
Developing a strong knowledge of all data sources and maintaining relations with data users/processors across the business.

Essential:

Extensive experience and expertise in Python.
Proven experience in modern code development practices and implementation of MLOps strategies in the cloud to drive operational and infrastructure cost efficiencies.
Hands on experience designing, building, and maintaining ML infrastructure and taking ML models to production.
Working knowledge of CI/CD practices.
Strong stakeholder management skills: working collaboratively with Data Science, DevOps, Data and Software Engineering teams.
Demonstrable experience of critical thinking, communication and problem-solving ability, and stakeholder management.
Coaching junior engineers to support their technical skills development.
Able to separate BAU from core objectives.
Demonstrate delivery under pressure and to deadline.

Preferred:

Experience productionising recommender engines and ensemble ML in Google Cloud
Experience or exposure to managing ML Infrastructure costs and operational reliability in the cloud.
Willingness to learn new tools and techniques; proactively keeping up to date with latest thinking and practices in the data science space.
Experience working in an e-commerce/retail market would be beneficial.
Embrace change with a positive outlook.
Confident to challenge, and receptive to being challenged.
The ability to thrive in a fast-paced environment.

Join Our Global Team: We invite you to apply for the position at RE Partners. Join us in shaping the future of business technology consulting and transforming the way organizations thrive in a digital world. As a diverse, woman-owned global business, we pride ourselves on keeping talent happy – our 7% attrition rate speaks volumes. Bring your talented friends along and earn a referral bonus

Equal Opportunity Employer: We are an equal opportunity employer and welcome applications from all qualified individuals regardless of race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, or veteran status.