Predictive Analytics and Modelling:

Conduct pattern analysis and predictive modelling to identify trends and opportunities.
Develop machine learning algorithms and statistical models to address business challenges.

Collaboration and Solution Development:

Collaborate with Program Managers and Business Analysts to understand business needs.
Translate business requirements into analytical solutions and actionable insights.

Data Collection and Coordination:

Lead efforts in data collection, preparation, and validation to ensure data quality.
Coordinate with data scientists, BI developers, and IT teams for comprehensive data management.

Solution Testing and Implementation:

Test and validate analytical models and solutions using historical and real-time data.
Pilot solutions with Business Analysts and end-users, gathering feedback for iterative improvement.
Engage with internal stakeholders including O&M teams and project SPOCs for data validation and analytics requirements.
Present complex findings and technical concepts to non-technical stakeholders in a clear and concise manner.

Training and Development:

Identify training needs within the organization related to analytics and data science.
Conduct knowledge sharing sessions and workshops to enhance analytical capabilities across teams.

Vendor Management and External Collaboration:

Manage relationships with external vendors for analytics tools and services.
Collaborate with industry experts and research institutions to stay updated on emerging trends and best practices in data analytics.

Compliance and Performance Tracking:

Implement tracking mechanisms to monitor compliance and performance of analytical solutions.
Gather feedback from stakeholders to continuously improve and refine analytics processes.

Project Management and Operations Support:

Provide project management support by defining project scopes, timelines, and resource requirements.
Ensure adherence to quality standards, processes, and best practices in analytics implementation.

Qualifications:

B.E/ B.Tech (Mechanical, Power Engineering, or related field), Advanced Degree (MBA, M.Tech) preferred

Experience:

Minimum of 5-8 years in data science, with at least 3 years with strong proficiency in Python required.

Industry Prefered:

Background in power utilities or similar sectors with proven track record in deploying analytics models into production and scaling them for real-time data handling.