Kpler

Commodities, Energy, Maritime

PowerMLEngineer

Tokyo, Japan FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“Power ML Engineer at Kpler. Skills: Python, Machine Learning, Data Science, ML Engineering. Adapt existing models to Japanese market. Integrate market signals”

What You'll Achieve.

empower organisations to make informed decisions; stay ahead in a dynamic market landscape; impactful results; enhance the quality of our forecasts

Industry & Context.

Commodities, Energy, Maritime
Problems you'll solve

simplifying global trade information; providing valuable insights; transform intricate data into actionable strategies

What They're Looking For.

Must Have

Understanding of electricity grid fundamentals, Data focused Software Engineer, Python, Postgresql or similar data stores, Building and consuming RESTful APIs, Machine Learning research, Data Science research, ML Engineering, Git, code reviews, Agile methodologies, time-series, events data, normalization, database design, command of written and spoken English, performance optimization, caching strategies

Nice to Have

Previous experience in electricity markets, AWS (or another cloud provider), Terraform, orchestration tool Airflow on a production environment, containerization (Docker), orchestration (Kubernetes), ML registry framework (e. g. MLFlow…), hexagonal architecture, medallion architecture, monitoring solutions (Datadog, Grafana, etc. . . ), NoSQL database, Japanese speaker, FastAPI

What You'll Do.

Adapt existing models to Japanese market

Integrate market signals

Build monitoring and alerting tools

Improve monitoring and alerting tools

Ensure API performance

Discuss roadmap with product team

Help team build plans

How You'll Work.

Team & Collaboration

Discuss the roadmap in collaboration with the product team; Help the team build ambitious yet sustainable plans; work asynchronously with team members in other countries

Communication Scope

command of written and spoken English; Great communication

Full Job Description

## Description At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors. Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 700 experts from 35+ countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success. As a Power ML Engineer you will be responsible for 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 Learn and adapt existing models to the Japanese market 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 Ensuring good performance on our API to distribute the data to end users Discuss the roadmap in collaboration with the product team. Help the team build ambitious yet sustainable plans ## Experience & Background Essential: Understanding of electricity grid fundamentals (Production, Transmission, Markets) Circa 3-5 years' of experience as a data focused Software Engineer Significant experience working with Python (FastAPI is a plus) Have worked with with Postgresql or similar data stores, Proficiency in building and consuming RESTful APIs Machine Learning research (Training, Evaluation, Backtesting, Tuning, Model selection). Deep learning is a plus, but not r

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