What We Are Looking For:

- 7-12+ years of relevant experience in Data Science, Machine Learning, Quantitative Research, Forecasting, Optimization or Algorithmic Decision Systems.
- At least 3+ years of experience leading complex Data Science / ML projects or technical teams.
- Proven experience building and deploying production-grade machine learning systems.
- Strong experience with time-series forecasting and predictive modelling.
- Experience working with large, noisy, high-frequency or real-time datasets.
- Experience solving problems where modelling accuracy has a direct financial or operational consequence.
- Demonstrated ability to convert ambiguous business problems into mathematical or algorithmic solutions.
- Experience with Indian electricity markets would be valuable, particularly familiarity with:

- Indian Energy Exchange IEX
- Day-Ahead Market DAM
- Real-Time Market RTM
- Green Day-Ahead Market – GDAM

- Open Access

- SLDC scheduling

- DSM / deviation management

- Renewable power procurement

However, prior power-sector experience is not mandatory. Exceptional candidates from quantitative trading, commodities, optimization, industrial AI, forecasting or other algorithmic decision-making environments are strongly encouraged to apply.

Core Technical Qualifications

1. Strong Quantitative Foundation The candidate should have strong knowledge of:

- Probability and statistics
- Time-series modelling
- Regression
- Classification
- Machine learning
- Predictive modelling
- Model validation
- Backtesting
- Experiment design
- Uncertainty estimation

The candidate should understand when and why to use different modelling approaches rather than simply applying predefined algorithms. 2. Forecasting

Strong experience with

- Time-series forecasting
- Multivariate forecasting
- Demand forecasting
- Price forecasting
- Probabilistic forecasting
- Forecast uncertainty
- Ensemble modelling
- Seasonal and event-driven forecasting





Experience with modern forecasting approaches such as gradient boosting, recurrent neural networks, transformers or other deep-learning architectures would be valuable. 3. Mathematical Optimization

Strong experience or aptitude in mathematical optimization is highly desirable.

This may include

- Linear Programming
- Mixed Integer Programming
- Constraint Optimization
- Stochastic Optimization
- Dynamic Programming
- Bayesian Optimization
- Multi-objective Optimization
- Reinforcement Learning

Experience applying optimization to real-world decision problems will be highly valued. 4. Programming

- Advanced proficiency in Python
- Strong proficiency in SQL
- Solid software engineering fundamentals
- Ability to write clean, scalable and maintainable production code
- Experience working with large data pipelines and ML infrastructure

Candidates may have experience with frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost or LightGBM, but we are not hiring based on familiarity with a specific library. We care more about the candidate's ability to choose the right approach and build reliable systems.

5. Production Machine Learning The candidate should have strong experience with:

- Production deployment of ML models
- Data and feature pipelines
- Model serving
- Batch and real-time inference
- Model monitoring
- Drift detection
- Automated retraining
- Experiment tracking
- Versioning
- Quality assurance
- Model-performance monitoring

Experience with cloud-based ML infrastructure and MLOps systems is preferred.

Education Qualification

- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Operations Research, Electrical Engineering, Economics, Computational Finance, Applied Mathematics or a related quantitative field.
- A Master's degree or PhD is preferred, particularly in:
- Machine Learning
- Statistics
- Operations Research
- Optimization
- Applied Mathematics
- Power Systems
- Computational Finance

📌 Lead Data Scientist (Noida)
🏢 Probus Smart Things
📍 Noida