ML Engineer (Forecasting)
New
Fully remote working arrangement across Europe, including the United Kingdom.Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Advanced English
- Experience
- 4+ years
- Required Skills
- AWSPythonSQLGCPMachine LearningAzure
Requirements
- 4+ years of experience in Machine Learning, Data Science, or applied AI roles.
- Strong expertise in time-series forecasting techniques and models such as ARIMA, Prophet, LSTM, or equivalent approaches.
- Advanced Python programming skills with experience using libraries such as Pandas, NumPy, scikit-learn, and PyTorch.
- Hands-on experience deploying machine learning models into production environments.
- Solid understanding of data preprocessing, feature engineering, and statistical modeling for time-series data.
- Experience working with cloud platforms such as AWS, GCP, or Azure.
- Strong knowledge of SQL and version control systems such as Git.
- Ability to work independently in ambiguous environments with strong problem-solving and analytical thinking skills.
- Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
- Advanced English proficiency required.
Responsibilities
- Design, develop, and deploy machine learning models focused on time-series forecasting and demand prediction use cases.
- Build scalable data pipelines and ML workflows using cloud platforms such as AWS, GCP, or Azure.
- Develop and optimize forecasting models including ARIMA/SARIMA, Prophet, LSTM, and other advanced predictive approaches.
- Perform data preprocessing, feature engineering, and exploratory analysis on complex healthcare and operational datasets.
- Collaborate with cross-functional teams including data engineers, product managers, and cloud specialists to deliver robust solutions.
- Participate in the full project lifecycle, from problem definition and proof of concept through to production deployment and stakeholder presentation.
- Ensure model reliability, scalability, and performance in production environments.
- Continuously evaluate and improve forecasting accuracy using appropriate metrics and validation techniques.
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