This is a Head of ML and Data position with a primary mandate to build, deploy, and own the machine learning capability from the ground up. The data lake and core platform infrastructure are in place. The ML layer is not. This person inherits a small internal team, sets the direction, and builds out the ML capability while also elevating the maturity, governance, and internal profile of the broader data function.
The person who thrives here is not just technically strong. They can stand in a room of senior stakeholders who all think they know data and ML, and convincingly explain why the right approach takes six months and not six weeks.
What You Will Own
* Building and deploying production‐grade machine learning models against IoT device data, starting with a predictive maintenance model already in early development that needs to be taken to enterprise grade and eventually packaged as a service to enterprise customers. This is the primary mandate of the first six to twelve months.
* Owning the data platform strategy: cloud infrastructure, storage architecture, data quality management, anonymisation, archiving, and governance frameworks. The business runs on AWS and the expectation is that this person will be fluent in that environment. Expect the role to be approximately 60% traditional data engineering and platform work and 40% machine learning, particularly in the early stages.
* Running and growing the data and ML function: You will mentor and direct the team, not just contribute technically.
* Establishing a data council. Right now, teams are solving data problems independently. This person will consolidate that, establish shared definitions and standards, and position the data function as the go‐to resource for analytics and insight across the organisation.
* Engaging with enterprise customers and internal stakeholders at a senior level. This includes presenting model performance, data strategy, and analytics insights to executive teams and large external clients. The ability to explain complex technical work to non‐technical audiences is not a nice‐to‐have here. It is core to the job.
What We Are Looking For
* Hands‐on production ML engineering. You have personally built, trained, validated, and deployed a model in a live environment. You can speak to the training data strategy, accuracy trade‐offs, and how the model performed in the field. Time‐series or IoT device data experience is a strong advantage.
* Proven data platform build experience. You have designed and built a data lake or warehouse from scratch, not inherited one. AWS, Snowflake, Airflow, and dbt are familiar territory.
* Strong AWS capability across the data and ML stack. SageMaker, S3, and adjacent services are hands‐on experience, not just awareness.
* Demonstrated ability to operate at senior stakeholder level. You have presented to executive teams, built business cases for technology investment, shaped data strategy conversations, and influenced decisions at a level beyond technical delivery. You are comfortable being challenged by non‐technical executives and know how to win that room.
* Experience leading or mentoring a small technical team.
* Domain background in IoT or connected devices is useful but not required. The right candidate from financial services, telco, utilities, healthcare technology, or any sector where data at scale and production ML intersect will be considered seriously.
If interested, please apply with your most up‐to‐date CV and I will reach out.
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