Data Scientist

Job Description:

Role Overview

The Senior Data Scientist will lead advanced analytics, machine learning, AI-driven insights, and data

innovation initiatives across MeitY projects. This includes building scalable models, enabling data-driven

decision-making, and supporting national-level digital initiatives, public service delivery platforms, and

mission-mode projects.

Key Responsibilities

A. Data Strategy & Solution Design

• Lead the design and development of end-to-end data science solutions for government projects.

• Identify opportunities for predictive analytics, AI/ML implementation, and process optimization.

• Collaborate with Ministry stakeholders, domain experts, and technical partners to translate policy

goals into analytical frameworks.

B. Machine Learning & AI Development

• Build, train, deploy, and optimize ML/DL models for classification, forecasting, clustering, NLP,

anomaly detection, and recommender systems.

• Develop responsible and explainable AI models aligned with government standards.

• Implement LLM-based solutions for automation, citizen service enhancement, and internal analytics.

C. Data Engineering & Architecture

• Work with Data Engineering teams to define data pipelines, ingestion workflows, and quality

frameworks.

• Ensure data readiness, metadata management, and adherence to MeitY cyber security and data

governance guidelines.

• Work with cloud environments (Azure/AWS/GCP), especially Azure Fabric/Fabric Data Engineering

for analytics projects.

D. Analytics, Reporting & Insights

• Deliver actionable insights for policy formulation, monitoring, and evaluation.

• Build dashboards, analytical reports, and KPI frameworks for senior leadership.

• Support development of national-level analytical platforms and real-time monitoring systems.

E. Governance & Compliance

• Ensure compliance with MeitY guidelines, data security norms, and IT Act provisions.

• Ensure ethical use of AI, model transparency, and adherence to Responsible AI principles.

F. Mentoring & Leadership

• Guide junior data scientists, analysts, and interns.

• Conduct knowledge-sharing sessions, create reusable templates, and drive best practices.

Required Skills & Competencies

Technical Skills

• Strong proficiency in Python, SQL, PySpark; familiarity with R is a plus.

• Expertise in ML/DL frameworks: Scikit-learn, TensorFlow, PyTorch, Keras.

• Experience with NLP tools: HuggingFace, spaCy, NLTK.

• Hands-on experience with cloud services—Azure preferred (Azure ML, Fabric, Data Factory,

Databricks).

• Experience with large datasets, data lakes, and distributed computing.

• Strong understanding of statistics, probability, optimization, and model evaluation.

• Experience with Visualization tools: Power BI, Tableau, or Azure Fabric dashboards.

Soft Skills

• Strong analytical and problem-solving skills.

• Excellent communication and stakeholder engagement ability.

• Ability to work in a fast-paced, multi-stakeholder government environment.

• Clear documentation and presentation skills.

Experience & Qualifications

• Education:

o / in Computer Science, Data Science, Statistics, Mathematics, or related fields; OR

o Master’s Degree in a quantitative discipline.

• Experience:

o 8+ years in Data Science/AI/ML.

o Experience in large-scale government, public sector, or enterprise projects preferred.

o Experience working with sensitive datasets and secure environments is desirable.

Preferred Experience in MeitY/ Government Projects

• Exposure to Digital Public Infrastructure (DPI), national-level portals, or mission-mode projects.

• Understanding of e-governance frameworks, data governance, and public data platforms.

• Experience with citizen-centric analytics or program monitoring dashboards (., PMG, NeGP, Digital

India).

Key Deliverables

• ML models and analytical solutions deployed in production.

• Automated data pipelines and model monitoring dashboards.

• Insights and reports for policy and program evaluation.

• Documentation, reusable code repositories, and SOPs.

• Capacity-building for internal teams.