Machine Learning Quant Data Scientist Energy Trading - Bonhill Partners

eFinancialCareers; Greater London; On-site; GBP 60,000 - 90,000; Full time

Job summary

We are partnering with a leading energy trading organisation to recruit a talented Machine Learning Quant/Data Scientist to join its Analytics team.

You will develop forecasting models for power, gas and other energy markets, applying machine learning and statistics to predict prices and demand. You’ll analyse large datasets from market and weather sources and provide insights to support Front Office trading decisions, working with Trading, Quant Analytics and Technology teams.

Qualifications

• PhD in Computer Science, ML, Data Science, or a highly quantitative field.
• 1–3 years' commercial experience as a Data Scientist, ML Engineer, or Quantitative Analyst.
• Experience forecasting/predictive modelling using machine learning techniques.
• Strong Python programming with Pandas, NumPy and Scikit-learn.

Responsibilities

• Develop and improve forecasting models for energy markets (power and gas).
• Apply ML and statistics to predict prices, demand and other market variables.
• Analyse large datasets from market, weather, generation and trading sources to identify predictive patterns.

Skills

Python

Pandas

NumPy

Scikit-learn

Time-series

Predictive modelling

Statistics

Education

PhD in CS/ML/DS

Tools

Pandas

NumPy

Scikit-learn

Job description

We're partnering with a leading energy trading organisation to recruit a talentedMachine Learning Quant/Data Scientist to join its growing Analytics team. This is an exciting opportunity for an early-career professional with a strong academic background and a passion for applying machine learning to real-world energy markets. Working alongside experienced Data Scientists, Quantitative Analysts and Traders, you'll help develop forecasting models that support trading decisions across power, gas and other energy markets.

The Role

As a Machine Learning Quant/Data Scientist, you'll contribute to the development and enhancement of forecasting models using machine learning and statistical techniques. You'll analyse complex datasets, identify market signals and help deliver predictive insights that support Front Office trading activity. You'll work collaboratively across Trading, Quantitative Analytics and Technology teams, gaining exposure to both the technical and commercial aspects of energy trading.

Key Responsibilities

1. Develop and improve forecasting models for energy markets, including power and gas.
2. Apply machine learning and statistical techniques to predict prices, demand and other key market variables.
3. Analyse large datasets from market, weather, generation and trading sources to identify predictive patterns.

You'll ideally have:

1. A PhD in Computer Science, Machine Learning, Data Science or another highly quantitative discipline.
2. 1–3 years' commercial experience as a Data Scientist, Machine Learning Engineer or Quantitative Analyst.
3. Experience developing forecasting or predictive models using machine learning techniques.
4. Strong Python programming skills and experience with common data science libraries such as Pandas, NumPy and Scikit-learn.
5. A solid understanding of statistics, time-series analysis and predictive modelling.