In partnership with multiple stakeholders, you will focus on developing and delivering leading advanced and edge analytics solutions using Google Cloud Platform engineering the ML solutions required to get a fully operational product, with a strong emphasis on extracting actionable insights from complex datasets as used by our utility customers.
As a member of our machine learning practice, you will be part of a small team of data scientists and ML engineers which is working with a DevOps culture within our product team and drive innovation for our utility customers.
Areas of Responsibility / Tasks
Design and implement modern, robust scalable ML solutions using a range of new and emerging technologies from Google Cloud Platform.
Define and support the research and analytical process with the entire data science workflow, from data exploration and feature engineering to model development and evaluation, to deliver business insights.
Responsible for advanced statistical analysis, machine learning, and predictive modeling.
Develop data driven analytical use cases in cooperation with other team members build Machine Learning models that can be used for network grid asset health and grid operations.
Work with Agile and DevOps techniques and implementation approaches in the delivery of ML projects.
Liaise and be part of our Google Cloud practice, contributing in the knowledge exchange learning programme of the platform.
Expectations for Skills & Experience
Experience in Google Cloud will be a plus, preferably using BigQuery or similar data warehouses as well as BigQuery ML and Kubeflow Pipelines or Vertex AI to run ML models.
Cloud Certifications will be a plus.
Programming experience with either SAS/R/Scala or Python.
Experience with advanced data analysis, statistical and machine learning modeling techniques.
Experience with data science and machine learning projects with ability to implement deep learning models.
Ability to initiate and drive analytics projects from inception to delivery, knowledge of MLOps and workflows to deliver and deploy ML operational solutions.
E2E Data Engineering and Lifecycle (including non-functional requirements and operations management) is a plus.
Experience working with CI/CD pipelines and test automation will be a plus.
Experience with utility time series data and forecasting will be a plus.
Behavioral Competencies.
Strong analytical skills, attention to detail and excellent problem solving/troubleshooting skills.
Excellent verbal and written communications skills.
Highly driven, positive attitude, team player, self-learning, self-motivating and flexibility.
Strong customer focus.
Flair for creativity and innovation.

Technical Lead, Data Scientist

Job Description

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