ML Engineer (Forecasting) | NDA

Accountabilities:

• 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.

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.

• Competitive contract-based compensation aligned with experience and project scope.


• Fully remote working arrangement across Europe, including the United Kingdom.


• Opportunity to work on real-world healthcare forecasting systems with measurable operational impact.


• Exposure to end-to-end machine learning lifecycle, from research to production deployment.


• Collaboration with experienced engineers and data professionals in a high-impact environment.


• Hands-on experience with modern ML stacks and cloud-native technologies.


• Flexible engagement structure with potential for extension beyond the initial project duration.


• Opportunity to contribute to meaningful AI applications in healthcare and operations optimization.

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