Data Scientist

• 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.

• Proficient in Python and SQL; hands-on with Pandas, NumPy, scikit-learn; basic exposure to TensorFlow/PyTorch is a plus.

• 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.

• 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.

• 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.