Data Scientist

Overview

Execute end-to-end data science and machine learning workflows under guidance to deliver measurable value in industrial operations, focused on time-series forecasting, anomaly detection and predictive maintenance.

What you'll do

• 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, time-series methods such as ARIMA/Prophet, anomaly detection, ensembles) and 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 following coding standards, version control and experiment tracking practices.
• Create visual analyses and concise summaries to communicate findings and recommendations to teammates and stakeholders, and incorporate feedback to iterate.
• Assist in measuring outcomes against success metrics (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).

What you'll need

• Bachelor's Degree in Computer Science or a STEM major (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; hands-on with Pandas, NumPy and scikit-learn.
• 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.
• Competent in visual analytics for EDA and communicating insights.
• 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 who explains technical information to teammates and stakeholders and asks clarifying questions.

Nice to have

• Generative AI (GenAI) as an added advantage.
• Exposure to operations in Oil & Gas, Fossil Power or Renewable Power.
• Basic exposure to TensorFlow/PyTorch.
• Familiarity with cloud ML platforms (e.g., AWS SageMaker, Azure ML, GCP Vertex AI) and MLOps concepts (experiment tracking, model registry, monitoring).
• Experience with dashboards or notebooks.
• Familiarity with big data/streaming technologies (e.g., Spark, Kafka) and real-time analytics considerations.
• 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).

Details

• Location: Bengaluru.
• Relocation assistance provided.