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ML Ops
1 month ago
We are looking for smart, motivated people to join our team who share our belief that we can outperform the energy sector, have a positive impact on the planet and have fun doing it together.
We have a vacancy for an ML Ops / Data Engineer to join our team.
ML Ops / Data Engineer
Oxford (Hybrid working, 1 day per week onsite)
Perm
£75,000pa - £80,000pa
You will be responsible for:
• Supporting Data Engineering Infrastructure: Contribute to the design, development, implementation and continuous improvement of our data engineering tools, workflows, processes, and platforms. This includes enhancing the architectural foundations and integrating new data management technologies.
• Writing Well-Structured Code: Develop clean, maintainable, well-documented code that adheres to best practices. Mentor and support in the continued improvement of coding practices within the Data Science team.
• Enhance data engineering knowledge: Improve expertise within the software team and ensure their ability to support and collaborate on the data science infrastructure.
• Data Quality Management: Continuously enhance data quality across multiple dimensions such as accuracy, availability, performance, and accessibility to ensure a clear understanding of data within the company.
• Providing backup/escalation to the tech-on-call team.
• Communicating effectively across Software and Data Science teams.
We’re looking for someone who is a great fit for our company. We want people who take accountability, build trust and are innovative. We encourage you to apply even if you may not meet every requirement in this posting. We value diversity and our environment is supportive, challenging and focused on the consistent delivery of high quality, meaningful work.
Requirements:
• 5+ years of Python experience.
• 3+ years of working in technical teams, building data pipelines, delivering productionised code, building/maintaining live applications, developing tooling and improving backtesting frameworks.
• 2+ years of orchestrating machine learning workflows.
• Proficiency with Orchestration and IaC in AWS (e.g. Terraform, Kubernetes, RabbitMQ, Airflow), Git, containerisation (Docker), database management (e.g. Postgres, Alembic).
• Fluent in Python and its wider numerical ecosystem (e.g. Pandas, NumPy, Polars, Pydantic).
Extra credit if you can demonstrate:
• Relational database design.
• Cloud infrastructure experience.
• Experience with monitoring frameworks (e.g. Prometheus).
• Experience archiving data to Parquet on S3 and creating tools for API/Grafana queries.
• Experience centralising diverse datasets for analytics, visualisation and machine learning.
• Familiarity with time-series forecasting and/or optimisation.
• Experience with data visualisation and dashboards (e.g. Grafana, Superset).
• Familiarity with machine learning and associated techniques (feature engineering, boosting methods, LightGBM).
In return, we’ll give you a competitive salary, flexible working arrangements and a lot of personal development opportunities. We operate a hybrid working model with at least 1 day per week in our offices in Oxford