Data Scientist

GE Vernova, Inc.; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Benefits offered by this job

Relocation assistance provided

Job summary

GE Vernova, Inc. in Bengaluru seeks a Data Scientist to execute end-to-end DS/ML workflows in industrial operations, focusing on data acquisition, feature engineering, model development, validation, and deployment collaboration with data/platform teams.

Responsibilities include building models (time-series, regression, classification), ensuring reproducible experiments, coding in Python/SQL, and communicating insights to stakeholders; GenAI adoption and industrial context experience are a plus.

Qualifications

• Bachelor's degree in Computer Science or STEM with 3 years of DS/ML experience.
• Strong Python and SQL with Pandas, NumPy, and scikit-learn.
• Experience with time-series, regression, classification, and anomaly detection.
• Familiarity with cloud ML platforms and MLOps concepts is a plus.
• Experience in data cleaning, feature engineering, and ETL basics.

Responsibilities

• Execute DS/ML tasks end-to-end across the model lifecycle with reproducible workflows.
• Train, tune, and validate models (regression, classification, and time-series).
• Collaborate to build data pipelines and model serving; support deployment activities.
• Produce clean Python/SQL code; follow coding standards and version control.
• Create visual analyses and concise summaries to communicate findings to stakeholders.
• Measure outcomes against KPIs and maintain project artifacts.
• Learn industrial context and data sources; identify data quality issues.
• Participate in POCs including GenAI analytics workflows.

Skills

Python

SQL

Pandas

NumPy

scikit-learn

Education

Bachelor's degree in Computer Science or STEM

Tools

TensorFlow

PyTorch

AWS SageMaker

Azure ML

GCP Vertex AI

Spark

Kafka

Job description

Job Description Summary

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.

Job Description

Roles and Responsibilities

• 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.
• Train, tune, and validate models (regression, classification, and time-series methods such as ARIMA/Prophet; anomaly detection; ensembles). Document experiments and results clearly.
• 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; incorporate feedback to iterate.
• Assist in measuring outcomes against success metrics (e.g., 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.

Education Qualification: 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.

Desired Characteristics

Technical Expertise:

• Proficient in Python and SQL; hands-on with Pandas, NumPy, scikit-learn; basic exposure to TensorFlow/PyTorch is a plus.
• 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.
• Competent in visual analytics for EDA and communicating insights; experience with dashboards or notebooks preferred.
• Familiarity with big data/streaming technologies (e.g., Spark, Kafka) and real-time analytics considerations is a plus.

Domain Knowledge:

• Exposure to industrial operations (Oil & Gas, Fossil Power, Renewable Power) is a plus; ability to learn failure modes, maintenance strategies, and process constraints and translate them into features and validation criteria.
• Basic understanding of business drivers and operational KPIs (availability, MTBF/MTTR, throughput, energy yield, emissions, cost) with the ability to connect analytical results to business value.

Personal Attributes:

• Curiosity and continuous learning mindset; connects ideas and incorporates feedback quickly.
• Comfort in ambiguity at project/task level; states assumptions clearly and adapts based on new information.
• Clear communicator; explains technical information to teammates and stakeholders and asks clarifying questions to ensure shared understanding.

Additional Information

Relocation Assistance Provided: Yes