machine learning engineer for time-series forecasting

Grid Dynamics provides technology consulting, platform and product engineering, AI, and advanced analytics services. The company helps enterprise organizations solve complex technical challenges and achieve business transformation through expertise in enterprise AI, data, analytics, cloud and DevOps, application modernization, and customer experience.

• Own the end-to-end DS/ML project lifecycle from initial research through robust production deployment;
• Build and sustain deployable statistical and machine learning models, primarily for time-series forecasting in integrated business planning;
• Manage and optimize legacy code while collaborating with core developers and business stakeholders;
• Design, implement, and enhance high-quality feature engineering pipelines and data processes.

• Proven ability to transition, deploy, and scale statistical and ML models in production;
• Deep proficiency in Python and PySpark;
• Experience with model lifecycle tracking tools such as MLflow;
• Strong hands-on cloud development experience with AWS, GCP, or Azure;
• Solid understanding of ML algorithms and core Python packages, including TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, pandas, NumPy, and SciPy;
• Strong knowledge of feature engineering;
• Solid software engineering background;
• Comfort working with legacy codebases and bridging developers with the business side;
• Nice to have: Experience with AI application development or production-level AI tool deployment.

• Competitive salary;
• Flexible schedule;
• Medical insurance and sports benefits;
• Corporate social events;
• Professional development opportunities;
• Well-equipped office.