The Sr. Data Scientist will work in cross-functional teams to design, develop, and deploy advanced analytics solutions across the Energy domain — including power generation, oil & gas, and industrial manufacturing. You will apply statistical modeling, machine learning, optimization, forecasting, anomaly detection, and NLP techniques to solve high-value business problems at scale.
In addition, you will have the opportunity to explore and apply modern Generative AI approaches (LLMs, Retrieval-Augmented Generation, and agent-based orchestration) where appropriate to accelerate insights and productivity — with a balanced focus on both research thinking and production delivery.

Job Description

As a Sr. Data Scientist, you will be part of cross-disciplinary team on commercially facing development projects, typically involving large, complex datasets. These teams include data scientists, software engineers, product managers, domain specialists, and end users — working together to deliver measurable business value.

Typical application areas include remote monitoring solutions on carbon management, carbon reduction solutions based on operations optimization, predictive maintenance, financial & operational risk modeling, intelligent decision support etc.,

In this role, you will:

• Develop scalable analytics and machine learning solutions to address optimization, forecasting, anomaly detection, and NLP needs.

• Translate concepts and analytical approaches into commercially viable products and services in collaboration with software developers and engineers.

• Design and conduct exploratory and targeted analyses to understand data behavior and uncover actionable insights.

• Contribute to model lifecycle activities including feature engineering, model training, validation, deployment, and monitoring (MLOps).

• Work closely with data engineers on data quality assessment, data preparation, and data pipelines.

• Document your work clearly (reports, notebooks, annotated code) and communicate findings to technical and non-technical stakeholders.

• Mentor junior team members and support best-practice adoption across the team.

• Engage with customers to understand requirements, present results, and shape solution direction.

• (Advantage) Explore and prototype modern Generative AI capabilities such as:

  • LLM-powered applications

  • Retrieval-Augmented Generation (RAG)

  • Multi-agent orchestration for automation
    — with attention to safety, reliability, and business value.

Education Qualification

Bachelor's Degree in Computer Science or “STEM” Majors (Science, Technology, Engineering and Math) with advanced experience.

Desired Characteristics

• Strong proficiency in at least one analytics or programming language (e.g., Python).

• Demonstrated experience in data cleansing, data quality assessment, and analytical data preparation.

• Hands-on experience applying descriptive statistics, feature engineering, and predictive modeling to real-world/industrial datasets.

• Ability to present insights clearly through visualizations and compelling storytelling.

Technical Expertise

• Experience with Python, PyTorch or TensorFlow, and ML lifecycle tools such as MLflow.

• Awareness of data management and data engineering best practices.

• Awareness of real-time or near-real-time analytics deployment patterns.

• Working knowledge of cloud concepts (AWS basics preferred) for service deployment.

• Exposure to GenAI / LLM concepts (RAG, LLM apps, multi-agent orchestration) — nice to have, not mandatory.

Domain Knowledge

• Awareness of industry and technology trends in Power Generation, Oil & Gas, or Manufacturing.

• Understanding of customer value drivers, performance metrics, and stakeholder needs in industrial contexts.

Leadership

• Proven ability to function effectively within collaborative, cross-disciplinary teams.

• Demonstrated problem-solving capability and critical thinking.

• Ability to influence through data, communicate trade-offs, and help guide decisions.

• Experience mentoring junior team members.

Personal Attributes

• Comfortable operating in ambiguous environments and iterating toward clarity.

• Balanced mindset between research exploration and production-grade delivery.

• Willingness to travel occasionally when business needs require (advantage).

Additional Information