Data Science Engineer

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
• Strong 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