[thumbnail]
Our 2021 intake of analysts is now open to applications.
Some more details about the roles and how to apply are given below.
We are seeking full-time data analysts to join the growing team at FPS, you will be working with other analysts and engineers to use operations and energy data to select and deploy the most effective technologies to decarbonise our customers’ operations.
You will also contribute to the development of a unique real time EV and energy asset management platform we are developing for the retail and logistics sector.
Background:
Logistics (including trucks, ships, warehouses and shops) is a vital enabler of global trade and urban living. However, not only is it energy intensive, globally consuming 12,400TWh per year or 7.5x the UK’s total energy usage, but it also contributes to nearly 10% of global CO2 emissions. To avoid dangerous climate change whilst maintaining our way of life, it is essential that we find ways to decarbonise logistics.
Flexible Power Systems (FPS) combines large scale data analytics, simulation, and engineering to build fact driven decarbonisation solutions for our clients who are some of the UK’s largest retailers and logistics providers.
The Role:
As a data analyst, we will help you develop and use your skills to work with our team to organize, visualize and model with large data sets relating to real world operations. You will also contribute to the development of the company’s EV and energy management platform.
Your role will involve:
- Data Management: importing, cleaning and organising raw data to enable robust analysis to take place.
- Data Analysis: working with technical experts from the transport, operations, and energy sectors to analyse customer operations and identify carbon saving opportunities
- Modelling and Simulation: developing equipment and operations simulations using large volumes of logistics and energy consumption data to test the impact of different technology choices.
- Visualizations and Reports: to help see and understand our customer’s data in a way that enables us to identify opportunities for energy, cost and emissions savings as well as effectively communicating these opportunities to our clients.
- Creating business opportunities: integrating analysis, simulation and technologies into solutions for our customers.
Requirements:
The role requires significant quantities of quantitative data analysis and so a degree level qualification or higher in a numerate discipline (e.g. computer science, economics, engineering or physics) is necessary. Ideally your degree or work experience will have enabled you to build expertise collecting, cleaning and processing data, as well as implementing algorithms for analysis or optimisation.
Whilst we will provide training and support, useful software skills include:
- Python
- Matlab
- Java Script
- R
- Excel & VBA
- Azure
- Sharepoint or other KM systems.
- PowerBI/Tableau
Prior experience of the following is desirable:
- Client/customer interactions
- Project management
- Database environments
- Optimisation Problems
Experience or interest in the following fields would be highly beneficial:
- Energy systems (vehicles, stationary (on and off grid))
- Mechanical and electrical energy storage and generation technologies
- Logistics (warehousing, vehicles or retail environments)
Further Details:
- Salary grade for this role is £28-35k depending on experience and qualifications.
- The company operates a pension scheme that it contributes 3% of salary to your 5%.
- The Company has an EMI compliant share option scheme to allow employees to benefit from the company’s success.
- Holiday entitlement is 25 days p.a. excluding bank holidays following probation.
- The Company currently operates remote working but its offices are in South West London near Clapham
- The role will also require travel to UK and overseas clients and partners from time to time.
Application Process:
Please submit a CV and covering letter to info@flexpowerltd.com.
Successful CVs will progress to interviews in London with FPS’s leadership team.