About the role
- Work with stakeholders to frame business and scientific questions as well-defined data science problems with clear success metrics.
- Build, train and validate statistical and machine learning models for forecasting, nowcasting, spatial interpolation and anomaly detection across environmental data.
- Combine and clean data from ground sensors, satellites, weather models and third-party sources, handling gaps, drift, outliers and calibration issues.
- Engineer features from large spatio-temporal datasets, including time series, geospatial rasters and point data.
- Design rigorous evaluation: backtesting, cross-validation, error analysis and comparison against ground truth.
- Work with engineering to deploy models to production, and monitor their accuracy, drift and cost over time.
- Run exploratory analyses and experiments, and turn findings into clear recommendations for product and business teams.
- Build reusable pipelines, notebooks and tooling that make the team's work reproducible and faster.
- Stay current with research in machine learning, remote sensing and climate science, and bring the best ideas into our products.
- Document methods and results so engineers and non-technical stakeholders can understand and trust them.
- Bachelor's or Master's degree in Engineering in Computer Science, or a degree in a relevant field such as Statistics, Mathematics, Physics, Data Science, Atmospheric or Environmental Science.
- 1+ years of professional experience in data science or applied machine learning.
- Strong foundations in statistics, probability, linear algebra and machine learning.
- Strong Python skills and hands-on experience with pandas, NumPy, scikit-learn, and at least one deep learning framework such as PyTorch or TensorFlow.
- Experience with time series forecasting and modelling, using classical methods (ARIMA, state space models) and ML approaches (gradient boosting, neural networks).
- Good SQL skills and experience working with relational or NoSQL databases.
- Experience taking models from prototype to production, working with engineers on APIs, monitoring and retraining.
- Experience working with large, messy, real-world datasets and turning them into reliable insights, including optimising code, queries and memory use for speed and scale (vectorisation, chunking, partitioning, efficient formats like Parquet).
- Ability to design sound experiments and evaluate models honestly, including uncertainty and failure cases.
- Familiarity with Git, code reviews and writing clean, tested, reproducible code.
- Clear communication of complex results to technical and non-technical audiences.
- Experience with big data technologies such as Apache Spark, Databricks, Hadoop, Dask or Kafka, and processing datasets that don't fit on a single machine.
- Experience building data pipelines (ETL/ELT, batch or streaming) with tools like Airflow or similar orchestrators.
- Experience with geospatial and remote sensing data (GeoPandas, xarray, rasterio, GDAL, NetCDF, GRIB) and satellite products.
- Background in atmospheric science, meteorology, air quality, hydrology or other environmental domains.
- Experience with cloud platforms, especially AWS (S3, SageMaker, EMR, Athena, Lambda).
- MLOps exposure including MLflow, Docker, model registries, CI/CD for models and observability.
- Experience with LLMs and generative AI frameworks (for example LangChain, LlamaIndex, OpenAI or Anthropic APIs, vector databases).
- Publications, Kaggle results or open-source contributions in relevant areas.
- You work independently. You take open-ended problems, break them down and drive them to a shipped model with little supervision.
- You're rigorous. You question your data, test your assumptions and care about whether a model is actually right, not just whether the metric looks good.
- You think about production from day one: accuracy, latency, cost and how a model behaves when the data changes.
- You communicate clearly with engineers, scientists and business stakeholders alike.
- You're driven by the impact of your work, love digging into hard problems, and are always keen to learn something new.
- Work on real-world problems with large environmental datasets from sensors, satellites and weather models, used by customers worldwide.
- Build models that help people and businesses respond to climate risks.
- High ownership, a small and fast-moving team, and room to grow into technical leadership.
- Exposure to data engineering, AI/ML and cloud infrastructure alongside core product work.