Join a cross-functional Data Science team delivering cutting-edge machine learning solutions in the field of time series forecasting. You will be part of a long-term initiative aimed at building scalable and high-quality ML-driven applications for a global client from the [finance/pharma/tech – choose depending on client sector. The project emphasizes model robustness, modular software design, and continuous integration/deployment of ML pipelines.

Data Science / Machine Learning Engineer – Python

Your responsibilities

• Design, develop, and maintain machine learning models using Python.
• Implement and evaluate supervised learning models and hybrid approaches.
• Work with time series data and contribute to forecasting solution development.
• Collaborate with software engineers to transform prototype models into production-ready software packages.
• Apply object-oriented programming principles to structure scalable codebases.
• Contribute to the design and execution of model evaluation processes (e.g., validation sets, overfitting mitigation).
• Participate in CI/CD processes, unit testing, and ML pipeline deployment.
• Cooperate closely with data scientists, DevOps engineers, and domain experts.

Our requirements

• 3+ years of professional experience using Python in ML solution development.
• Solid understanding of machine learning techniques and algorithms (e.g., supervised learning, train/test splits, overfitting, evaluation metrics).
• Ability to write clean, efficient, and modular Python code.
• Hands-on experience with object-oriented programming (OOP).
• Basic understanding of time series analysis principles.
• Experience in structuring ML code into maintainable software packages.
• Knowledge of or willingness to learn time series forecasting techniques.
• Familiarity with Deep Learning frameworks applied to forecasting (e.g., TensorFlow, PyTorch, GluonTS, Prophet).
• Exposure to ML engineering tools and practices (e.g., CI/CD pipelines, unit testing, MLflow, Airflow).