• End-to-End Model Ownership: Drive the entire machine learning lifecycle, from exploratory data analysis (EDA) and advanced feature engineering to model training, validation, deployment, and post-launch monitoring for performance and concept drift.
• Problem Formulation: Translate ambiguous business requirements and domain challenges into well-defined technical problems, testable hypotheses, and robust machine learning solutions.
• Rigorous Experimentation: Design, test, and validate multiple modeling approaches to find the optimal solution, establishing clear and relevant evaluation metrics that directly align with business goals.
• Technical Implementation & Deployment: Utilize our Triple AI SageMaker environment to efficiently train, deploy, and manage scalable models in a production setting.
• Data Storytelling & Visualization: Communicate complex model outputs and data-driven insights through compelling storytelling and clear visualizations, empowering business stakeholders to make informed, data-backed decisions.
• Product-Oriented Mindset: Develop a deep understanding of the business domain and product vision, ensuring that your work is not just technically sound but also delivers tangible and measurable value to the end-user. 

• Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 3+ years of hands-on professional experience in a data science role focused on building and deploying machine learning models.
• Strong proficiency in Python and its core data science libraries (e.g., pandas, NumPy, scikit-learn, Matplotlib/Seaborn).
• Solid proficiency in SQL for complex data querying, transformation, and analysis.
• Experience building models for business applications such as forecasting, classification, clustering, or regression.
• Familiarity with at least one major cloud platform (AWS, GCP, Azure).
• Proven experience implementing and managing model monitoring systems to detect data and model drift in a production environment is highly desired.
• A forward-looking interest in the application of Generative AI, with an enthusiasm to learn how to combine LLMs and other generative techniques with traditional machine learning as part of your professional development in the role.
• Hands-on experience with MLOps principles and tools (e.g., MLflow, Kubeflow, feature stores). 

• Participation in interesting and challenging projects
• Flexible working hours
• A great, non-corporate atmosphere
• Stable employment conditions (contract of employment or B2B contract)
• Opportunities for development and promotion
• Attractive package of benefits
• Remote work

We reserve the right to contact the selected candidates.