The Data Scientist executes end-to-end Data Science and Machine Learning workflows under guidance to deliver measurable value in industrial operations. You will focus on data acquisition/cleaning, feature engineering, exploratory analysis, model development/validation, and contribute to deployment and monitoring in collaboration with data/platform engineering. Primary use cases include time-series forecasting, anomaly detection, and predictive maintenance, with Generative AI (GenAI) as an added advantage. Exposure to operations in Oil & Gas, Fossil Power, or Renewable Power is a plus.

Execute DS/ML tasks across the model lifecycle: data acquisition, quality assessment/cleansing, feature engineering, and exploratory data analysis on industrial datasets (sensor/telemetry, logs, emissions, maintenance) with reproducible workflows.

Collaborate with data/platform engineering to contribute to data pipelines and model serving; support deployment activities and basic monitoring/drift checks under supervision.

Produce clean, well-structured Python/SQL code; follow coding standards, version control, and experiment tracking practices.

Create visual analyses and concise summaries to communicate findings and recommendations to teammates and stakeholders; reliability, availability, efficiency, emissions, cost) and maintain project artifacts (reports, annotated code).





Learn industrial context, data sources, and domain constraints; proactively identify data quality issues and propose fixes within established procedures.

Participate in POCs/pilots, including GenAI/LLM-assisted analytics workflows (analytics automation, documentation) as an added advantage.

Bachelor's Degree in Computer Science or 'STEM' Majors (Science, Technology, Engineering and Math) with minimum 3 years of experience in Data Science/Machine Learning or closely related roles.

Proficient in Python and SQL; Applied experience with feature engineering, model selection, cross-validation, and performance measurement for time-series and classification/regression problems.

Solid foundations in data management: ETL basics, data quality checks/cleansing, and working with large datasets.

Familiarity with cloud ML platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI) and MLOps concepts (experiment tracking, model registry, monitoring) is a plus.

experience with dashboards or notebooks preferred.

Familiarity with big data/streaming technologies (e.g., Exposure to industrial operations (Oil & Gas, Fossil Power, Renewable Power) is a plus; Comfort in ambiguity at project/task level; Relocation Assistance Provided: Yes

📌 Data Scientist (Python, ML, SQL) (Bengaluru)
🏢 GE Vernova
📍 Bengaluru