ML Engineer (Forecasting & Applied Data Science)

Design, develop, and maintain production-scale forecasting and time series models to predict demand and other key business metrics. Build and optimize robust data pipelines to ensure the quality, availability, and traceability of models in production. Collaborate with marketing, retail, and pricing teams to translate complex business requirements into actionable machine learning solutions.

Implement and monitor scalable MLOps practices within cloud environments to guarantee model performance and reliability. Perform exploratory data analysis and customer segmentation using transactional, digital behavior, and geospatial data sources. Document technical processes and communicate complex analytical results clearly to non-technical stakeholders.

Senior-level expertise with 5+ years of experience in Data Science or Machine Learning Engineering, with a strong focus on time series forecasting and predictive modeling. C1 English level or higher. Proficiency in Python or R for statistical modeling and machine learning.

Hands-on experience deploying and monitoring machine learning models in a major cloud environment, preferably GCP or Azure. Strong statistical foundation, including experience with time series models, survival analysis, and customer segmentation. Advanced SQL skills and experience working with relational databases.

Proven experience leading technical projects or mentoring junior data professionals. Degree in Statistics, Engineering, Data Science, Mathematics, or a related quantitative field. Hands-on experience with AWS, MLOps, Data Pipelines. Skills Python R SQL Machine Learning Time Series Forecasting GCP Azure AWS MLOps Statistics Data Pipelines

Power BI GIS Geospatial Analytics Survival Analysis Customer Segmentation