Role & responsibilities

Job Description

High-Level Job Description

1) Essential experience: Forecasting + end-to-end model building
- Proven delivery of capacity/demand/time-series forecasting solutions (e.g., workforce/headcount and capacity planning, volume forecasting, infrastructure utilisation, contact-centre demand).
- Solid hands-on capability to design, build, train, and validate models end-to-end (not GenAI-only), including feature engineering, model training, tuning, and handover for deployment/productionisation.
- Demonstrated depth in relevant forecasting techniques, such as ARIMA/SARIMA, Prophet, XGBoost/LightGBM for time series, LSTM/Temporal CNN, hierarchical forecasting, etc. (approach may vary; rigour and depth are key).

2) Strong machine learning fundamentals
- Solid understanding of core ML concepts (e.g., epochs, loss/error metrics, overfitting, cross-validation).
- Time-series best practices: time-ordered train/test splits, awareness of data leakage, and handling trend/seasonality, missing data, and outliers.

3)



Forecast evaluation and operational readiness
- Clear experience using forecasting metrics and validation practices, including MAPE/SMAPE, MAE/RMSE, prediction intervals, backtesting, and monitoring for drift/retraining triggers.
- Ability to translate forecasts into operational decisions (e.g., capacity planning assumptions, scenario modelling, and what-if driver analysis).

4) Technical stack and engineering maturity
- Proficiency with the Python forecasting/ML ecosystem: pandas, numpy, scikit-learn, statsmodels, Prophet, plus PyTorch or TensorFlow where deep learning is applied.
- Production mindset: version control, reproducibility, documentation, and basic familiarity with MLOps practices (even if lightweight).

Mandatory SkillsMLDesirable Skillsforecasting,Python,ML OPS,Pandas,Capacity Planning

Thanks,

Digvijay Sunil

[email protected]

📌 Ai Ml Engineer (Pune)
🏢 Coforge
📍 Pune