We are looking for a highly skilled and versatile ML Engineer to join our advanced analytics team. In this role, you will design, develop, and deploy recommendation systems, time series forecasting models, and machine learning solutions based on boosting and decision tree algorithms. You will work closely with cross-functional teams to turn data into actionable insights and scalable solutions. Key Responsibilities:Develop and optimize recommendation systems (collaborative filtering, content-based, hybrid approaches)Build and validate time series forecasting models using traditional and machine learning techniques (ARIMA, Prophet, LSTM, etc.)Implement boosting algorithms (XGBoost, LightGBM, CatBoost) and decision trees for various supervised learning tasksCollaborate with data engineers and ML engineers to deploy models on Azure and Databricks environmentsPerform data exploration, feature engineering, and model evaluationPresent findings and models clearly to technical and non-technical stakeholdersStay up to date with the latest tools and methodologies in applied machine learningProfile RequirementBachelor's or Master’s degree in Data Science, Computer Science, Engineering, Statistics, or a related fieldProven experience with recommender systems and time series modelsStrong knowledge of boosting algorithms and decision trees, C++Proficiency in Python and libraries such as scikit-learn, pandas, NumPy, statsmodelsExperience with Azure cloud services and DatabricksStrong problem-solving skills and ability to work independentlyFluent in English (spoken and written)