Lead Data Analyst

Job Description Summary

Many employers promise the chance to make a difference - at GE Vernova, you can change the world. Bringing clean, affordable power to the developing world, decarbonizing the world's electricity network, helping to build the grid of the future powered by renewable energy ... they're all part of our company's strategy. If you're passionate about applying AI, and excited to tackle UN SDG-7,13 and Energy Transition challenges, as well as motivated by the prospect of shaping the future of energy industry through innovation and new business models, we encourage you to apply. Join us in our journey to redefine what's possible with AI and make a lasting impact on the world of Energy.

We are seeking a dynamic, forward-thinking and results-driven Data Analyst for Grid automation to enable the development of differentiated innovative solutions accelerating the energy transition. This role will work on collecting, analyzing and interpreting data to extract insights for building advanced model for grid innovation applications. Reporting to AI leader in CTO organization, the Data Analyst will work in close collaboration with GA product lines, R&D teams, product management and other GA functions.

• Preparation of data by collecting it from various sources (e.g., databases, csv), cleanse and transform.
• Applying advance statistical techniques to identify patterns, correlation and trends in the data and derive meaningful insights.
• Create visualization through graphs, dashboards, charts etc. to communicate insights to the leadership through reports and provide platform for Explainable AI.
• Develop framework to handle missing and outlier data.
• Collaborate with cross-functional teams of product management, R&D, and other functions, to understand their needs and develop innovative solutions.

• PhD/Master's/Bachelors degree in computer science, electrical engineering, specifically in the computer and electric power engineering field with hands-on experience in data science.
• + years Experience of working in professional working environment and knowledge of Statistical techniques, artificial intelligence (AI) and machine learning (ML), including, unsupervised learning, supervised learning, and reinforcement learning, Deep learning, large language models (LLMs).
• Develop automated data quality checks, anomaly detection, and error resolution mechanisms
• Proficiency in various relational and non-relational databases.
• Proficiency in statistical algorithms and data visualization techniques.
• Able to share ideas and work well in a team environment, proactive approach to tasks displaying initiative.
• Flexible and adaptable; open to change and modification of tasks, working in multi-tasking environment.
• Optimize database performance, indexing, partitioning, and query tuning for large datasets.
• Ensure data security, access control, and compliance with regulatory requirements.

• Industry deployment experience
• Ability of programming tools or languages, such as Python, R, SQL, Excel, CSV etc.
• Experience with data visualization tools (e.g., Tableau, Power BI, GGPlot etc.).
• Experience in data mining techniques.
• Familiarity with machine learning algorithms.
• Strong analytical skills with the ability to collect, organize, analyse, and disseminate significant amounts of information with attention to detail and accuracy.
• Experience in GraphDB, MongoDB, SQL/NoSQL, MS Access, databases.
• Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
• Strong analytical and problem-solving skills
• Experience of ETL/ELT pipeline development using tools like Apache Airflow, DBT, or similar frameworks.
• Knowledge of CI/CD practices and infrastructure-as-code for data pipeline deployment.
• Customer-focused mindset with a commitment to delivering high-quality data solutions.
• Strong communication skills and a proactive and open approach to conflict resolution.
• Strong organizational skills, self-motivated, and self-directed.