ML Engineer - Power

As a Machine Learning Engineer in the Power team, you will be instrumental to the development of the Power offering. You will be fully dedicated to a new product covering the Japanese power market. This product will adapt and expand as required, existing modelling infrastructure to the Japanese power market in order to build fundamental forecasts.

Key Responsibilities

• Develop a deep understanding of existing models and adapt them to the Japanese Power market
• Develop new models, managing the entire lifecycle (Training, Evaluation, Backtesting, Tuning, Model selection etc).
• Integrate market signals from other commodities (LNG, coal) to enhance the quality of our forecasts
• Enrich the product and participate in the development of new features
• Build and improve monitoring and alerting tools and dashboards
• Discuss the roadmap in collaboration with the product team. Help the team build ambitious yet sustainable plans

Experience & Background

Essential:

• BSc/MSc in computer science, computer engineering or equivalent
• Circa 3-5 years' of experience as a data focused Software Engineer (minimum 2 Years experience with Machine Learning)
• Significant experience working with Python
• Experience with Postgresql or similar data stores
• Machine learning R&D experience (Training, Evaluation, Backtesting, Tuning, Model selection)
• Data Science R&D experience (Statistics, Hypothesis testing)
• ML Engineering experience (model versioning, feature versioning)
• Comfortable working with Git, code reviews, and Agile methodologies
• Experience with time-series, events data, normalization, database design
• Understanding of performance optimization and caching strategies
• Great communication, ability to work asynchronously with team members in other countries
• Strong command of written and spoken English

Desirable:

• Previous experience of the electricity/power markets
• Have experience with AWS (or another cloud provider), using Terraform
• Experience with Deep learning techniques
• Experience with orchestration tool Airflow on a production environment
• Experience with containerization (Docker) and orchestration (Kubernetes)
• Experience with a ML registry framework (e.g. MLFlow…)
• Knowledge on hexagonal architecture and medallion architecture
• Used monitoring solutions (Datadog, Grafana, etc...)
• Experience with NoSQL database
• Japanese speaker