Data Scientist

Role Overview

Data Scientist to drive digital transformation within the renewable energy domain by building advanced analytics, predictive models, optimization engines, Data Mining, and AI & ML Ops-driven solutions. This role requires a blend of strong data science expertise and deep understanding of renewable energy systems such as solar PV, wind turbines, battery storage, smart grids, and energy forecasting.

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

Mandatory Skills

Strong proficiency in:

• Cloud: AWS S3
• Handon Experience on LLMs and Gen AI/Agentic AI
• Machine Learning/Statistics: Strong understanding of algorithms (supervised/unsupervised) and statistical modeling (LSTM, ARIMA etc)
• ETL/ELT Tools: ADF, Databricks, Snowflake pipelines
• Big Data Technologies: Spark,
• Programming & Framework : Python, SQL and libraries such as Scikit-learn, TensorFlow, or PyTorch, LangChain etc
• Solid understanding of data modeling, distributed systems, and data lifecycle management.
• Experience with CI/CD automation for data pipelines.Good To Have Skills
• ETL/ELT Tools: Glue, Snowflake pipelines
• Big Data Technologies: Hadoop, Kafka, Hive

Responsibilities

• Build predictive maintenance models using sensor/SCADA to detect failures and optimize maintenance cycles.
• Create optimization algorithms for asset performance.
• 2. Data Management & Feature Engineering
• Work with large datasets from SCADA, and O&M Perfomance tools
• Engineer domain-specific features such as degradation factors, turbine efficiency, wake effects, shading patterns, etc.
• Evaluate data quality and collaborate with engineering teams on sensor reliability.
• 3. Digital Product Development
• Contribute to the development of digital platforms such as:
• Asset performance monitoring dashboards
• AI‑enabled O&M tools
• Build reusable models, APIs, or pipelines for integration with cloud solutions.
• 4. Collaboration & SME Interaction
• Work cross-functionally with mechanical, electrical, and operations engineers to validate assumptions and model outputs.
• Translate technical insights into clear business recommendations for stakeholders.
• Support R&D initiatives on energy efficiency and decarbonization.
• 5. Visualization & Reporting
• Develop interactive dashboards and visualizations using GRAFANA, Power BI, Tableau, or Python libraries.
• Communicate findings through technical reports, presentations, and data stories.