Group Head - Data Science

Key Responsibilities :

- Design and implement predictive models to improve business outcomes such as power generation optimization, pricing strategies, cost reduction, and enhanced customer experiences.

- Apply advanced statistical modeling, machine learning, probability theory, algorithms, data mining, and natural language processing techniques.

- Utilize machine learning methods including but not limited to Clustering, Regression, Bayesian methods, Tree-based learners (Random Forest, XGBoost), SVM, Time Series Modeling, Dimensionality Reduction, Structural Equation Modeling (SEM), Generalized Linear Models (GLM/GLMM), Deep Learning, Neural Networks, Topic Modeling, Multivariate Statistics, K-NN, Na- ve Bayes, etc.

- Work with deep learning architectures and methods for simulation, scenario analysis, constraint optimization, anomaly detection, semi-supervised and unsupervised learning.

- Apply optimization techniques such as Linear Programming, Genetic Algorithms, Simulated Annealing, and Monte Carlo Simulation to solve complex problems.

- Explore and implement emerging technologies like deep learning, NLP/NLG, image/video processing, recommender systems,

chatbots, and voice AI.

- Lead the entire data science pipeline including problem scoping, data discovery, exploratory data analysis (EDA), modeling, evaluation, visualization, deployment, and continuous improvement.

- Collaborate with internal technical teams for seamless integration of AI/ML solutions into existing systems.

- Develop reusable and scalable machine learning assets and accelerators following best practices.

- Drive agile development processes (SCRUM) and apply MLOps principles for model deployment and lifecycle management.

- Continuously research and analyze market and industry trends in AI/ML technologies, proactively proposing innovative solutions.

- Handle coding, testing, debugging, and documentation of AI/ML applications and evaluate cloud technology options for analytics workloads.

- Lead future migration of analytics applications and pipelines to cloud platforms.

Qualifications & Skills :

- Masters degree in Statistics, Economics, Business Analytics, or related quantitative discipline.

- 8-15 years of proven experience in data science, machine learning, and predictive analytics.

- Expertise in a wide range of machine learning algorithms and statistical methods.

- Hands-on experience with deep learning frameworks and architectures.

- Strong programming skills in Python, R, or similar languages; proficiency in SQL.

- Experience with cloud platforms (AWS, Azure, GCP) and modern data engineering tools is preferred.

- Solid understanding of MLOps, CI/CD, and Agile SCRUM methodologies.

- Excellent problem-solving skills and ability to break down complex business problems into actionable data science solutions.

- Strong communication skills with the ability to translate complex technical concepts for business stakeholders.