Senior Machine Learning Engineer
3 days ago
Senior Machine Learning Engineer (Time Series + Surrogate Models) - Remote (UK-only) - Climate Technology 🏢🌳
Our client is transforming the commercial real estate sector by championing sustainability through innovative retrofitting solutions. Believing that the most sustainable buildings are those already standing, they empower commercial real estate owners with precise, low-intervention tools to optimize properties. Leveraging only building addresses, the company crafts tailored retrofit strategies that mitigate stranded asset risk, elevate property value, and secure advantageous financing and partnerships.
As a Senior Machine Learning Engineer, you’ll be integral to developing predictive models that guide retrofit efforts for diverse commercial buildings. You’ll design and implement machine learning solutions using energy, environmental, and structural data to forecast the outcomes of various retrofit strategies, helping clients transition to sustainable operations. This role also offers collaboration opportunities with Data Scientists, Building Scientists, and other Engineers, as well as project involvement beyond core time series work—such as natural language processing (NLP) for data workflows, cost modeling, and imagery analysis.
Key Responsibilities:
- Develop machine learning models to predict building performance enhancements from retrofit initiatives, using detailed energy, environmental, and structural data.
- Conduct feature engineering on time series data to improve model precision and clarity.
- Rigorously validate models to ensure reliability and generalization.
- Collaborate across disciplines to analyze building energy performance.
- Participate in code reviews and uphold best practices for model deployment.
- Support additional data processes, including NLP for data ingestion, retrofit cost prediction, and imagery-based analysis.
- Document and communicate technical insights and recommendations for both technical and non-technical stakeholders.
Qualifications and Experience:
Essential:
- Substantial experience in ML model development, particularly in applying machine learning to time series data, including:
- Feature engineering: Extracting meaningful features, managing missing data, and handling irregular time series.
- Time series pre-processing: Skilled in cleaning, normalizing, and transforming data, with knowledge of seasonal trends.
- Understanding of classical time series techniques: e.g., ARIMA, seasonality analysis, Kalman filters.
- Deep learning for time series forecasting: Proficiency with RNNs, LSTMs, GRUs, and TCNs for complex time series.
- Experience with multivariate time series: Applying ML methods to multivariate data, capturing cross-variable dependencies over time.
- Proficiency in Python and related libraries, including Pandas, Scikit-learn, TensorFlow, PyTorch, Darts, and sktime.
Bonus:
- Experience with building energy data or a background in the built environment or sustainability.
- Experience with Surrogate Models.
- Familiarity with MLOps practices.
- Knowledge of geospatial and remote sensing data and related tools (e.g., Xarray, Dask, GeoPandas).
- A commitment to climate action and sustainable development.
If interested, please apply with your latest CV, and a consultant will reach out to you. Please note that this role does not offer sponsorship at this time.
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