Machine Learning Engineer

About the job

We are looking for a Machine Learning Engineer to build a time-series forecasting system for demand prediction and revenue optimization. The solution will analyze historical sales, seasonality patterns, and external variables to generate accurate forecasts for business planning. The project includes feature engineering for temporal data, model comparison, and uncertainty estimation. Key Responsibilities: • Analyze historical timeseries datasets with seasonality and trend components • Engineer lag features and rolling statistical indicators • Implement forecasting models (ARIMA, Prophet, XGBoost, LSTM) • Compare classical statistical methods with ML/DL approaches • Evaluate performance using MAE, RMSE, and MAPE • Build forecasting dashboards for business stakeholders • Document modeling assumptions and forecast confidence intervals Requirements: • Strong background in timeseries modeling • Proficiency in Python and forecasting libraries • Experience with regression and sequence modeling techniques • Understanding of seasonality and trend decomposition Nice to Have: • Experience with probabilistic forecasting • Familiarity with cloud deployment workflows • Knowledge of realtime data pipelines Contract duration of less than 1 month. Mandatory skills: Python, Machine Learning, Data Science, Forecasting, Prophet, ARIMA, LSTM, Predictive Modeling, Time Series Analysis, XGBoost

Requirements

• Python
• Machine Learning
• Data Science
• Forecasting
• Prophet
• ARIMA
• LSTM
• Predictive Modeling
• Time Series Analysis
• XGBoost

Preferred Technologies

• Python
• Machine Learning
• Data Science
• Forecasting
• Prophet
• ARIMA
• LSTM
• Predictive Modeling
• Time Series Analysis
• XGBoost