Machine Learning ML Engineer

4 tygodni temu

Warszawa, mazowieckie, Polska emagine Polska Pełny etat 5 zł - 880 zł

  • Industry: Energy System

  • Location: preferably Poland

  • Workload: full-time

  • Model of working: 100% Remote

  • Contract: B2B, initially for 6 months + extensions



Summary: Main focus here is to develop and refine time series forecasting models to optimise energy asset performance within the Client’s optimisation platform, leveraging Machine Learning and Data Science techniques.



Responsibilities:


  • Develop and enhance time series forecasting models across multiple domains, including load, generation, and consumption.

  • Conduct exploratory data analysis (EDA) to understand data behaviour and quality, assessing trends, seasonality, autocorrelation, and identifying and addressing missing values or outliers.

  • Apply and compare both statistical and machine learning approaches to build robust, scalable forecasting pipelines.

  • Evaluate, monitor, and benchmark model performance using appropriate metrics to ensure model robustness over time.

  • Collaborate closely with optimisation and engineering teams to integrate forecasts into production decision systems and measure their real-world impact on asset performance.


Must Haves:


  • Strong understanding of time series forecasting principles, including univariate and multivariate modeling, and effective use of exogenous covariates.

  • Solid knowledge of exploratory data analysis (EDA) techniques for time series.

  • Ability to frame forecasting as a machine learning problem, including both regression-based and window-based prediction approaches, and to evaluate results rigorously.

  • Familiarity with modern time series forecasting frameworks and libraries, along with sound model evaluation and selection.

  • Proficiency in Python and experience building cloud-based ML pipelines (preferably on GCP, including Vertex AI).


Nice to Haves:


  • Experience with data visualisation tools to communicate insights effectively.

  • Knowledge of optimisation algorithms relevant in energy markets.

  • Familiarity with additional programming languages relevant to machine learning applications.


Other Details:

  • Team Structure: Small team with a bunch of models already running, with no business tasks/contact with stakeholders.

  • Project Context: Involves real-time decision-making systems for energy asset management.

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