As a Machine Learning Engineer in Retail, you will design and deploy scalable forecasting solutions that empower businesses to make smarter inventory, supply chain, and operational decisions.

Your work will bridge raw data and actionable insights, transforming complex time series datasets into production-ready models that anticipate demand, reduce waste, and optimize retail workflows.

If you are passionate about bridging cutting-edge machine learning with practical business applications, this role offers the perfect opportunity to make a significant impact.

REQUIREMENTS

• At least 2+ years of experience in Data Science / Machine Learning Engineer roles with a focus on deploying time series models to production, ideally in retail, CPG, or e-commerce.
• Proficiency in Python (e.g., pandas, scikit-learn, Statsmodels, LightGBM, etc.) and SQL. Hands-on experience with time series forecasting libraries (e.g., Prophet, Statsmodels) and familiarity with cloud data platforms (Databricks, Snowflake).
• Upper-Intermediate English with strong client-facing skills — you can lead meetings, clarify requirements, and explain technical trade-offs confidently.
• A background or keen interest in operations research for supply chain or inventory optimization.

RESPONSIBILITIES

• Design and develop robust pipelines for time series forecasting, handling and preparing large-scale datasets to drive actionable insights.
• Build and refine forecasting models from prototype to production, ensuring quality, scalability, and effective performance.
• Collaborate closely with stakeholders, to understand business needs, present results through visualizations and dashboards, and explain technical concepts in clear, actionable terms.
• Partner with data engineers to troubleshoot pipeline bottlenecks, improve data quality, and implement automated monitoring for data/model drift.

Please send your CV to the following email address: lizaveta.trushnikava@leverx.com