Data Scientist - Flexible working, hybrid working, returners welcome
Why do we have this role?
Resonate specialises in providing mission critical systems and data-led solutions, primarily in the transport industry. We currently deploy a range of digital solutions that are actively helping to optimise the running of the railway and drive real improvements in performance and reliability. This role sits right at the heart of our data activities, and you’ll bring creativity and a desire to make things work beyond your primary responsibility.
Joining our Data Insights team, you will be helping to provide a range of data-driven solutions that enable better decision making for both the business and our customers. We are looking for someone who is highly motivated by the prospect of making a real impact in this sector, hungry for the chance to play a prominent role on some tough modelling challenges, and who can help drive the team forward.
What are the key objectives?
- To develop robust data-driven solutions that can be integrated with our products to deliver a real impact for our customers.
- To demonstrate to key stakeholders that our solutions deliver value and are worth investment.
- To understand how we can use a wide variety of data sources and modelling techniques to positively influence the key decisions that our customers take.
- To work across the business to identify best opportunities to add value from the data we have.
- To ensure success as a team, through collaboration, mentoring and a flexible ‘can-do’ attitude.
Person Requirements Profile
- Track record of driving the development of solutions to novel and challenging modelling problems, and personally able to make progress.
- Enjoys not just solving a modelling problem, but of defining which problem it is most valuable to solve.
- Can work effectively as part of a close-knit team, sharing and discussing findings to ensure we reach the best outcomes.
- Strong presentation and communication skills, with the ability to translate insights from complex analysis into clear and concise business recommendations.
- A depth of knowledge of machine learning techniques and how to successfully apply them.
- A strong background in statistics and statistical analysis.
- Experience in writing version-controlled software (the team’s main language being Python).
- Can make efficient use of the tools and frameworks of data science to produce robust results at pace (the team make heavy use of Jupyter, pandas, sklearn, matplotlib and Sagemaker).
- An appreciation of AWS storage, compute, machine learning and database services.
Our Values, Behaviours, and Flexibility in How we Work
At Resonate it’s the ‘how’ we do things together with the ‘what’ we do that differentiates us. We want our people to engage with our vision and display our values and behaviours in their day-to-day activities.
Joining the team and contributing to our vision should not be determined by traditional views of working, our background, or our personal circumstances. What we do, when we do it, and where, should promote positive outcomes - meeting client commitments, innovating, and collaboration with colleagues – flexibility is key to this.
This role could be based in either London or Derby, and we have adopted a flexible, hybrid approach to work and work location - so please talk to us about your preferences and how these could work for both you and the team. Our normal full-time hours are 37 per week, but there is a great deal of flexibility over start and finish times, so this role may well suit an individual looking for an arrangement that suits their needs and commitments, around our current requirement to be in the office at least one day per week.
We are very open to different levels of experience in candidates as the team already has a range of capabilities that contribute to its success. Salaries offered are therefore flexible based on the value you can bring, and once with us, we support your ongoing development and keep a close eye on your progress.
Please submit your CV and supporting statement via the application method below.
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