Lead - Analytics and Platform Management

Purpose/Objective

To establish a robust analytics-driven decision-making capability for NMDPL, enabling the business to achieve its strategic objectives in both short and long term. The role involves developing Analytics and AI roadmaps for each business vertical, aligned with overall business and digital strategies. It includes designing, building, and optimizing data infrastructure and systems for efficient data storage, organization, and access, while ensuring seamless integration of AI solutions. The position provides a strategic and technical foundation for data management, driving operational efficiency, and delivering AI-driven solutions that enhance key value drivers and business outcomes.

Key Responsibilities of Role

Key Accountabilities - Partner with business stakeholders to identify data-driven opportunities and deliver scalable solutions. - Define and implement data architecture strategies for modeling, metadata management, data warehouses, and ETL environments. - Design, develop, and optimize physical data models ensuring performance and efficiency. - Review and enhance existing data architectures for compliance and effectiveness. - Research emerging technologies and incorporate innovative data modeling and AI/ML practices. - Architect and operate resilient data platforms aligned with enterprise data strategy. - Drive best practices in data governance, quality, privacy, and metadata management. - Collaborate with cloud vendors to build scalable, business-centric data stores. - Lead AI/ML solution design, integration with IIoT systems, and deployment strategies. - Provide technical leadership to analytics and platform engineering teams. - Develop digital solutions for Mining, Trading, and Metals domains using AI/ML. - Ensure compliance with regulatory standards and implement robust monitoring solutions. - Create roadmaps for migrating legacy systems to next-gen platforms. - Mentor and guide junior team members in technical development.

Technical Competencies

Cloud Technologies,Change Management,MLOps,Data/AI Lifecycle Management,Data Platforms

Qualifications and Experience

Job Skills & Qualifications - Experience: 15+ years in Data Architecture, Analytics, Big Data, Cloud Development, and AI solution deployment; 5+ years as Data/AI Architect. - Technical Expertise:- Data Warehousing, ETL/ELT, Data Pipelines, Relational Databases. - Big Data technologies (Hadoop, Spark, Kafka, Hive, MongoDB, Cassandra). - Cloud platforms (GCP, Azure) with focus on security and cost optimization. - Visualization tools (Power BI, Tableau, QlikView). - Programming (Python, Java), CI/CD, DevOps, MLOps. - AI/ML Skills:- Statistical modeling, machine learning, NLP, unstructured data analytics. - Familiarity with Bayesian models, clustering, neural networks, multivariate statistics. - Hands-on with data science toolkits (NumPy, Pandas, scikit-learn). - AI model development, deployment, API integration, transfer learning. - Leadership & Communication:- Strong stakeholder management and executive communication skills. - Ability to mentor teams and drive Agile delivery.