Data Science Lead/Manager- Immediate Joiner

Role & responsibilities

1.Lead and mentor a team of Data Scientists and Analysts in developing predictive,prescriptive, and generative Al models.
2.Establish end-to-end data science methodologies, including feature engineering, model selection, and perfomance evaluation..

3. Develop data-driven solutions for policy optimization, citizen engagement, and government service enhancement.
4.Define data model lifecycle standards, including model versioning, retraining, and explainability frameworks.
5. Collaborate with AI/ML Engineers to operationaliz machine learning models within production systems. Define and monitor Al performance KPIs such as accuracy, latency, and fairness.

Preferred candidate profile

Educational Qualification:

• B.Tech / M. Tech / M.S. / Ph.D. in Statistics, Mathematics, Computer Science, or any quantitative discipline. • Advanced certifications in Data Science, Machine Learning, or AI (e.g. AWS ML Specialty) preferred. • Research papers, case studies, or significant open source contributions(are preferred)

Experience:

10-12 years of total experience in data science and analytics.

6-8 years in advanced analytics, statistical modelling, and research-

driven problem solving.

Minimum 4-6 years leading data science teams or projects involving

large-scale model deployment.

Proven track record in implementing AI/ML models in production

environments within enterprise or government setups.

Technical Competencies:

Programming & Tools: Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow), R, SQL.

Advanced ML Techniques: Deep learning, ensemble methods, time series forecasting, NLP, computer vision, reinforcement learning, and causal inference

Al Techniques: Deep learning, NLP (Transformers, BERT, GPT), computer vision, anomaly detection.

Big Data & Analytics: Apache Spark, Databricks, Hadoop, Hive, ELT pipelines.

Visualization: Tableau, Power BI, Matplotlib, Seaborn. Research & Innovation: Literature review, research methodology, and evaluation Of emerging AI technologies

Model Lifecycle Management: MLflow, Data Version Control (DVC), and experiment tracking frameworks.

Model Evaluation: Advanced metrics, cross-validation strategies, bias detection, fairness evaluation, and model intemretability

Governance: Responsible AI design, model interpretability, and privacy-preserving AI (DP, FL)