Senior Data Scientist / Machine Learning Engineer
Location: London – Hybrid

About the Company

This innovative company focuses on tackling climate change by providing cutting-edge data solutions that help clients manage assets, properties, and infrastructure in the face of climate risks.

Using advanced climate science, remote sensing, and modelling, they forecast the impacts of extreme weather events like floods, storms, and subsidence. Their platform enables financial institutions and real estate organisations to assess risks, protect assets, and make smarter investment decisions.

With a strong commitment to diversity and fostering an inclusive culture, they’re building a team where creativity and problem-solving come together to address one of the world’s most pressing challenges.

The Role

As a Senior Data Scientist, you’ll work within a multidisciplinary team, including Data Scientists, Climate Scientists, and Geospatial specialists. Collaborating with Engineering and Product teams, your role will focus on enhancing the company’s NLP model—an integral part of its product offerings.

Key responsibilities include:

• Improving the existing code base and exploring new algorithms and techniques.
• Fine-tuning domain-specific LLM models to improve performance.
• Performing statistical analysis, model evaluation, and extracting insights from text data.
• Visualising findings and effectively communicating results across teams and to clients.

Essential Skills

• Experience in end-to-end machine learning model development in a product-focused role.
• Proficiency with ML algorithms (e.g., regression, classification, clustering) and Python ML libraries (e.g., sklearn, spaCy, NumPy, SciPy).
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines (e.g., GitHub Actions).
• Knowledge of data visualisation tools (e.g., Matplotlib, Seaborn, Tableau, or Power BI).
• Experience with cloud platforms (e.g., AWS, GCP, or Azure).
• Strong problem-solving skills and the ability to collaborate across teams and communicate insights effectively.

Desirable Skills

• Experience with Python web scraping tools (e.g., BeautifulSoup, Scrapy, Selenium).
• Exposure to MLOps frameworks (e.g., MLFlow, Weights & Biases).
• Understanding of financial services or real estate industries, particularly from a climate risk perspective.
• Knowledge of geospatial data processing tools (e.g., GeoPandas, GDAL) or GIS software (e.g., QGIS).

Benefits

• 💡 £1,000 annual training budget for professional development.
• 🏡 Flexible hours and hybrid working (3 days in-office, 10am–4pm core hours).
• 🏥 Mental health and wellbeing support via Oliva.
• 🏖 25 days holiday, plus Bank Holidays, Christmas closure, and half a day off for your birthday.