- 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
- Expectations for Skills & Experience .
- 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 robust 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.