Job Description:

Role : Senior Data Scientist

Location : Hyderabad(Work from Office)

Job Type : Full Time

Experience : 7+ Years

We are seeking an experienced Senior Data Scientist to lead the development of predictive analytics and time series forecasting models. The role will focus on building models that support decisions related to demand planning, inventory, pricing, capacity, resource allocation, risk identification, and operational performance.

The ideal candidate should be comfortable owning the full model lifecycle — from data exploration, feature engineering and model development to validation, deployment support, monitoring, retraining and business interpretation.

Key Responsibilities

1. Forecasting and Model Development

- Design, build and optimize time series forecasting models to predict business metrics such as demand, sales, inventory, pricing, capacity, volume or operational risk.
- Develop forecasting models using methods such as ARIMA/SARIMA, SARIMAX, Exponential Smoothing, Prophet-style models, regression-based forecasting and state-space models.
- Build advanced machine learning and deep learning forecasting models using approaches such as XGBoost, LightGBM, LSTM, TCN, DeepAR, N-BEATS, N-HiTS and Transformer-based models.
- Handle time series challenges such as trend shifts, seasonality, holiday effects, autocorrelation, sparse data, missing values and outliers.
- Implement hierarchical forecasting to maintain consistency between granular-level forecasts and aggregate-level business forecasts.

2. Predictive Analytics and Machine Learning





- Build complementary predictive models for classification, regression, anomaly detection and risk scoring.
- Perform exploratory data analysis and identify patterns, drivers and business-relevant insights.
- Engineer features from transactional, operational, external, geospatial, pricing or event-based datasets.
- Support experimentation, backtesting and causal analysis where required.

3. Model Validation, Monitoring and MLOps

- Evaluate model performance using metrics such as WMAPE, MAPE, MAE, RMSE, forecast bias, precision, recall and F1-score.
- Design backtesting frameworks for historical model validation.
- Productionalize models using containerization (Docker) and orchestration tools.
- Monitor model performance, data drift, concept drift and forecast bias after deployment.
- Support automated retraining pipelines, model versioning and model governance.

4. Stakeholder Collaboration

- Translate model outputs into clear business recommendations for non-technical stakeholders.
- Quantify forecast uncertainty using prediction intervals or confidence bands.
- Build explainable outputs that show key drivers behind forecasts or risk scores.




- Mentor junior data scientists and contribute to best practices in statistical modelling and forecasting.

Required Qualifications

- Master’s degree or Ph.D. in Statistics, Mathematics, Computer Science, Economics, Operations Research, Engineering or a related quantitative field.
- 5+ years of industry experience in data science, machine learning or predictive analytics.
- At least 3+ years of hands-on experience in time series forecasting.
- Solid understanding of statistical forecasting, seasonality, trend decomposition, autocorrelation, stationarity, ACF/PACF, model selection and forecast validation.
- Strong programming skills in Python and advanced working knowledge of SQL.
- Hands-on experience with libraries such as pandas, NumPy, scikit-learn, statsmodels, Prophet, Darts, sktime, PyTorch or TensorFlow.
- Experience with ML models such as XGBoost, LightGBM, CatBoost or similar.
- Experience working with large, multi-source datasets.

Preferred Qualifications

- Experience in supply chain, logistics, retail, agriculture, energy, fintech, pricing or public-sector analytics.
- Experience with hierarchical forecasting and forecast reconciliation.
- Exposure to probabilistic forecasting, Bayesian models or structural time series.
- Familiarity with cloud platforms such as AWS, Azure or GCP.
- Experience with MLOps tools such as MLflow, Airflow, Prefect, Docker, feature stores or model registries.

Experience building dashboards or monitoring views using Power BI, Tableau, Plotly/Dash or similar tools.

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