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Principal Data Scientist

Sector:  Energy, Water & Food
Division:  Energy



The Principal Data Scientist would analyse, model, and predict the behaviour and performance of electric utility systems across the entire value chain of generation, transmission, storage, distribution, and consumption using multiple data sources (grid, IoT, weather, etc.) and data types (operational, non-operational, enterprise).  The models will help build the “smartness” in the applications obeying physics and power-system first principles. Additionally, developing AI solutions for non-electric-grid use-cases (e.g., analysing customer feedback) as dictated by the business would be necessary.  Cognitive Digital Technology solutions (AI, blockchain, etc.)  thus developed using datasets of different sizes (raw/processed, big/small) would be containerized in the CI/CD pipeline thereby allowing for seamless development, integration, deployment, feedback, and continuous improvement.  The principal data scientist will play a pivotal role in defining the Cognitive Digital Solutions strategy of Energy Water and Food sector supporting the full value chain of the EWF sector working with the data architect and software engineering team / vendors to build these applications


Key Accountabilities and Responsibilities


  • Work closely together with various stakeholders (photovoltaic engineers, grid engineers, wind engineers, and market design personnel) and discover and develop use-cases across the entire value chain of Energy
  • Work with real (legacy, commercial, open and/or inhouse generated) and simulated field data (where data is scarce and needs augmentation or needed for PoCs / MVPs) from intelligent sensors and operational systems
  • Develop (hands on) and maintain signal processing, multiphysics and machine learning models for but not limited to:
  • prediction of solar/wind energy generation, diagnosis of faults and analysis of use
  • operation of energy storage, diagnosis of faults and analysis of use
  • smart grid behaviour and its components during steady-state, dynamic, and transient conditions.
  • Build scenario analysis to help create a digital twin of a target system and its components
  • Develop solutions that are cloud native but platform agnostic with a view to improve customer (internal and external) experience, improve system reliability, increase safety, enable DER integration, and accelerate business outcomes from grid digitalization initiatives.
  • Be abreast of the state-of-the-art solutions in the utility analytics field but experienced enough to develop/import and implement only those solutions which address business challenges using acceptable computing time and power
  • Support the analytics team with thought-provoking ideas and broader analytics know-how from sister verticals
  • Assist product engineering teams with validation and verification of end-to-end sensor system performance.
  • Engage consultants, vendors and consultants to build analytics partnership initiatives and roadmap development
  • Contribute to intellectual property development and company representations in technical conferences and trade shows
  • Publish internal reports and external articles and white papers to showcase company success and maintain industry thought leadership
  • Act as delegate authority for the EWF Director of AI and DA.



Education & Experience


  • PhD (preferred) / MS in Computer Science / Physics /Maths / Engineering / Statistics / Data Analytics / Data Science.  If multiple degree holder, preferably at least one degree must be in engineering / physics.
  • Proven track record of success, possibly backed-up by a positive referral.
  • Strong algorithmic problem-solving skills.
  • Excellent understanding of statistics – hypothesis testing, p-values, confidence intervals, regression, classification, forecasting and optimization.
  • Excellent understanding of Deep learning frameworks : Tensorflow and/or Pytorch
  • Expert knowledge of Python libraries for signal processing, time series modeling, database querying, statistics, machine learning and visualization
  • Experience in end-to-end ML model development and deployment is highly preferred.  Must know model containerization and integration with CI/CD pipeline
  • Track record of prior work and/or official coursework in the analytics space as demonstrated by publications in reputed journals, certifications, GitHub projects, etc. (blog will be a huge plus)
  • Excellent SQL scripting
  • Knowledge of NLP and ontologies a plus
  • Familiarity with electrical distribution system operations and management practices and technologies
  • Good knowledge of Utility industries & business processes
  • Good interpersonal, communication (English) and motivational skills to build teams and manage stakeholders
  • Ability to bring innovative ideas from inception to actual implementation through clear value propositions and roadmaps
  • Ability to operate in a team-oriented and collaborative environment and flexible to changes due to the greenfield nature of the projects.
  • Demonstrable minimum experience of 8 years working in data science with at least 2 years of contiguous experience in power/energy industry. (If PhD holder, part of PhD time can be considered as experience if substantiated)
  • Must have worked in a software / application development environment where analytics / AI models are deployed and operational. 
  • Strong understanding and collaborative work experience with solution delivery teams (eg. dev/ops, CI/CD, etc).
  • Experience in software development using Agile methods. 
  • Demonstrable experience into AWS / Azure or any other enterprise platforms for big data, AI, IoT and Machine Learning.
  • Experience in the field of data management / architecture is an advantage.

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