Data Scientist

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