Senior Asset Data Scientist
National Grid
- Bristol, England, United Kingdom
- Bristol, England, United Kingdom
À propos
Develop and implement models and algorithms to uncover trends, patterns, and insights in large, complex datasets. Play a key role in building our plans for our future data models and development plans that allow us to gather data in an efficient and accurate manner. Monitor model performance and recommend improvements to enhance accuracy. Contribute to processes supporting NGED’s Network Asset Risk Metrics and Common Network Asset Indices Methodology. Design methods to assess asset condition from diverse sources (e.g., reports, images, measurements) and apply insights across the asset fleet. Work with cross-functional teams to identify high-impact business challenges and deliver data-driven solutions. Conduct data analysis, interpret results, and provide actionable recommendations. Build predictive models using statistical and machine learning techniques to improve efficiency and performance. Collaborate with stakeholders to identify opportunities and support data-driven strategies. Mentor and guide junior team members on analysis, machine learning, and best practices. Stay up to date with industry trends and emerging technologies, assessing their impact on NGED’s analytics initiatives. Communicate effectively across NGED and externally to ensure timely delivery of insights. Ensure solutions align with data governance standards and emerging processes. Identify and exploit opportunities to improve organisational efficiency and effectiveness. Lead quality assurance for analysis and model development. Conduct cost-benefit assessments and measure benefits of implemented projects. Manage projects in line with NGED governance standards. Model assets and networks to improve outcomes and reduce lifetime asset costs. Ideal Candidate
A degree in Computer Science, Statistics, Mathematics, or a related field. Proven experience in a data science leadership role with the ability to mentor and develop data scientists. Ideally knowledge of NGED asset operations and network operations Experience working with large and complex data sets and a deep understanding of data science methodologies and best practices. Strong experience with machine learning techniques and predictive modelling, including experience with Python and/or R programming languages. Strong data visualisation and storytelling skills using tools such as Tableau, PowerBI, or similar. Experience working with large and complex data sets and a deep understanding of data science methodologies and best practices. Strong collaboration and communication skills with the ability to effectively present findings and insights to technical and non-technical stakeholders. Knowledge of the Common Network Asset Indices Methodology and Network Asset Risk Metrics process would also be advantageous. Evidence of your qualification certificates will be required prior to appointment. National Grid Electricity Distribution is committed to safeguarding the interests of the Company, colleagues and customers. This role is subject to a satisfactory Barring Service, (DBS) check, depending on the role different levels of screening and vetting are required. Some roles require a triannual check. #LI-SC1 About Us
We’re National Grid Electricity Distribution (NGED), the owner and operator behind the electricity distribution systems for the Midlands, the Southwest of England and South Wales. Serving communities of more than 8 million people, our expert teams deliver heat, light and power for homes and businesses. National Grid employs over 29,000 people worldwide. We are building an inclusive workplace, a place to actively celebrate the cultures, personalities and preferences of our colleagues – who in turn help to build the success of our business and reflect the diversity of the communities we serve. Our vision is to be at the heart of a clean, fair and affordable energy future and we are doing this in a fast-moving industry with an increasing focus on tackling climate change, exploring new energy sources that are renewable, low carbon, and improve efficiency to meet demand.
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Compétences linguistiques
- English
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