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For Companies At a Glance
- Tasks: Analyse energy data and develop Python-based models for forecasting.
- Company: Join SSE, a leading energy company committed to a net zero future.
- Benefits: Enjoy flexible work options, healthcare discounts, and generous family entitlements.
- Why this job: Be part of a team powering 3.9 million homes while making a positive impact.
- Qualifications: Tertiary education in Maths/Statistics; Python coding skills required.
- Other info: SSE values diversity and offers support for all applicants.
The predicted salary is between 49004 - 57728 £ per year.
Join to apply for the Forecasting Data Scientist role at Scottish and Southern Electricity Networks
Join to apply for the Forecasting Data Scientist role at Scottish and Southern Electricity Networks
Direct message the job poster from Scottish and Southern Electricity Networks
Base Location: Perth, Glasgow or Reading
Salary: £49,004 – £57,728 + a range of benefits to support your finances, wellbeing and family.
Working Pattern: Permanent | Full Time | Flexible First options available
The role
As a Forecasting Data Scientist, you are responsible for analysing energy forecasting and settlements data to support business decisions.
You will
- Perform energy forecasting and settlements data analysis, ensuring outputs meet quality standards and deadlines to support informed business and commercial decisions.
- Develop and maintain new and/or existing Python-based machine learning models to interrogate large volumes of settlements data to forecast future energy consumption.
- Working with a high degree of independence, implement novel solutions or appropriate mitigations as required by changing circumstance or requirement to ensure analysis remains relevant and timely.
- Engage with colleagues across the business to maintain a broad awareness of developments in the energy sector and how these will affect our commercial forecasting activities.
- Abstract, distil, and communicate key information succinctly in written or presented form proportionate to the audience.
You have
- A Mathematics and Statistics to tertiary education (preferably higher). Science or Engineering (related to the use and distribution of electrical energy) to secondary education minimum.
- The ability to code in Python working with large datasets as a minimum. Familiarity with standard Python packages (NumPy/Pandas/Scikit-learn etc.) and some knowledge of VBA and SQL would also be preferable.
- Competent user of specific Azure data related resources (or similar) including: Databricks and Storage Account (Data Lake).
- The ability to plan and develop new and existing models or analytical tools with a logical approach to design and maintenance.
- Advanced skills when using Microsoft Excel, with solid proficiency in MS Access and the broader Microsoft Office suite.
About SSE
SSE has a bold ambition – to be a leading energy company in a net zero world. We\’re investing around £10 million a day in homegrown energy to help power a cleaner, more secure future.
SSEN Distribution, part of the wider SSE group, powers 3.9 million UK homes and businesses. Our team of more than 4,200 skilled engineers, customer service advisors and energy experts keep customers connected to a safe, reliable supply of electricity while investing in and developing the flexible networks needed for our low carbon future.
Flexible Benefits To Fit Your Life
Enjoy discounts on private healthcare and gym memberships. Wellbeing benefits like a free online GP and 24/7 counselling service. Interest-free loans on tech and transport season tickets, or a new bike with our Cycle to Work scheme. As well as generous family entitlements such as maternity and adoption pay, and paternity leave.
Work with an equal opportunity employer
SSE will make any reasonable adjustments you need to ensure that your application and experience with us is positive. Please contact / 01738 341147 to discuss how we can support you.
We\’re dedicated to fostering an open and inclusive workplace where people from all backgrounds can thrive. We create equal opportunities for everyone to succeed and especially welcome applications from those who may not be well represented in our workforce or industry.
Ready to apply?
Start your online application using the Apply Now box on this page. We only accept applications made online. We\’ll be in touch after the closing date to let you know if we\’ll be taking your application further. If you\’re offered a role with SSE, you\’ll need to complete a criminality check and a credit check before you start work.
Seniority level
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Seniority level
Entry level
Employment type
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Employment type
Full-time
Job function
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Job function
Project Management, Analyst, and Information Technology
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Industries
Utilities, Oil and Gas, and Renewable Energy Semiconductor Manufacturing
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Forecasting Data Scientist employer: Scottish and Southern Electricity Networks
Contact Detail:
Scottish and Southern Electricity Networks Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Forecasting Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in energy forecasting and data analysis. This will not only help you understand the role better but also allow you to engage in meaningful conversations with the hiring team during interviews.
✨Tip Number 2
Brush up on your Python skills, especially with libraries like NumPy, Pandas, and Scikit-learn. Consider working on personal projects or contributing to open-source projects that involve large datasets to showcase your coding abilities.
✨Tip Number 3
Network with professionals in the energy sector, particularly those who work in data science roles. Attend industry events or join online forums to gain insights and potentially get referrals for the position.
✨Tip Number 4
Prepare to discuss how you've used data to drive business decisions in past roles. Be ready to share specific examples of your analytical work and how it has impacted outcomes, as this will demonstrate your value to the team.
We think you need these skills to ace Forecasting Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the Forecasting Data Scientist role. Emphasise your proficiency in Python, machine learning, and data analysis, as well as any experience with Azure resources.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for energy forecasting and your understanding of the energy sector. Mention specific projects or experiences that demonstrate your ability to analyse data and develop models.
Showcase Technical Skills: In your application, clearly outline your technical skills, especially in Python and relevant libraries like NumPy and Pandas. If you have experience with SQL or VBA, make sure to include that as well.
Communicate Effectively: Since the role requires distilling complex information, ensure your application is clear and concise. Use straightforward language and structure your documents logically to make it easy for the hiring team to follow your points.
How to prepare for a job interview at Scottish and Southern Electricity Networks
✨Showcase Your Technical Skills
As a Forecasting Data Scientist, you'll need to demonstrate your proficiency in Python and familiarity with libraries like NumPy and Pandas. Be prepared to discuss specific projects where you've used these skills, and consider bringing examples of your work to the interview.
✨Understand the Energy Sector
Research current trends and challenges in the energy sector, especially those related to forecasting and data analysis. Being able to discuss how these factors impact business decisions will show your engagement and understanding of the role.
✨Prepare for Problem-Solving Questions
Expect questions that assess your analytical thinking and problem-solving abilities. Practice explaining your thought process when tackling complex data sets or developing models, as this will highlight your logical approach and independence.
✨Communicate Clearly
Since the role involves distilling complex information for various audiences, practice summarising your past experiences succinctly. Use clear language and avoid jargon when discussing your work to ensure your points are easily understood.
