Machine Learning Engineer, Climate X

• Proven experience building and deploying end-to-end ML models (from data preparation to monitoring in production)
• Strong grasp of ML techniques (regression, classification, clustering), and strong experience with Python ML libraries (sklearn, spaCy, NumPy, SciPy etc.)
• Experience using Git for version control and familiarity with CI/CD pipelines
• Comfortable with data visualisation tools (Matplotlib, Seaborn etc.)
• Experience using cloud platforms (AWS, GCP, Azure) for ML pipelines
• Strong communication skills — able to explain technical details to non-technical stakeholders
• A collaborative mindset and eagerness to learn from others in a multi-disciplinary team

Desirable

• We’re helping the world become more resilient to the impacts of physical climate risks (including floods, fires, storms and more). In doing so, we help drive positive global impact aligned to many of the UN’s Sustainable Development Goals (SDG’s)
• Our team builds cutting-edge, peer-reviewed science (incorporating climate projections, remote sensing data) and translates that into financial impacts (to the value of assets or business disruption linked to failure of critical infrastructure) that our customers in the financial services industry use to identify, manage and mitigate those risks
• Climate X’s customers include the world’s largest banks, asset managers and insurance companies including CBRE, Standard Chartered, Virgin Money and Federated Hermes, as well as a partnership ecosystem that includes Deloitte, Capgemini and AWS. Combined, they manage over $6.5 trillion of assets
• Customer’s choose us thanks to our ecosystem of products that help solve real human problems, and drive tangible business benefits to our customers. They love our customer-centric mindset, as well as our pace of innovation in the market
• As Machine Learning Engineer, you will join an interdisciplinary team of other Data Scientists, Climate Scientists and Geospatial experts, collaborating closely with our Engineering and Product teams to deliver impactful products to our clients
• Develop and enhance existing ML codebases, especially around our NLP product
• Conduct research into new techniques and algorithms to optimise performance and accuracy
• Fine-tune domain-specific LLM models to meet business requirements
• Run statistical analyses to assess model performance and extract meaningful insights
• Build visualisations to communicate findings and facilitate wider understanding across the business and to our clients
• Core hours 10am-4pm