Purpose of the role
The Applied Data and Technology team at Connected Places Catapult proactively identify unaddressed problems, lowers barriers and promotes scalable solutions for the adoption of data-led decision making and innovative technologies across the public and private sectors towards better, more connected places.
In the data science team, we use our expertise in data processing, statistics, machine learning, multi-domain modelling and visualisation to go from place-based problems to appropriate and practical data solutions. We are collaborative with other teams and experts across the catapult, working very closely with specialists in housing, local planning, new mobility technologies, critical infrastructure, wellbeing and more. We believe combining data science with human-centred and ecosystem-centred approaches is key to successful data-led solutions that are problem-first, not led by technology.
Recently, we have explored the potential in mobile network data to contribute to better transport planning in rural areas with the Department for Transport; we have worked with our human-centred design team to explore what a golden thread of building safety information might look like, so that data about buildings and maintenance is better managed and action can be taken where this is not the case; we have worked with startups to build digital twins of rail stations to help operators meet the needs of people social distancing and we have supported a round of data science fellows working with local authorities.
We are looking for a collaborative, problem-focused data scientist to join our diverse technical team. We are open to a range of technical backgrounds but an interest in place-based problems is a must.
- Work collaboratively with other technical colleagues such as data scientists, data engineers, software engineers to deliver advanced, innovative data-centric solutions. We do lots of prototyping and exploratory work and then collaborate with partners to push ideas out into the world.
- Draw on experience of data science methods with awareness across: data processing, modelling, algorithmic, visualization, statistical and scientific approaches. Proactively research unfamiliar areas – we know data science is broad and fast moving, and that there is always more to learn to find the best solution.
- Be creative and open-minded in exploring data-rich solutions to problems, and be prepared to throw away ideas. Be open to building quick, imperfect prototypes. Innovation is uncertain!
- Work collaboratively using technical collaboration methodologies including agile project delivery and version control workflows.
- Advocate for scientific methodology across multidisciplinary projects – work with the rest of the team to define data science input to these projects.
- Be active and engaged in team discussions, contributing ideas and being proactive in helping to shape a strong team culture of sharing knowledge and helping others innovate.
- Encourage data science best practice in own delivery and across the team, contributing to team best practice on everything from data governance and ethics to version control, documentation and project structure to scalable and re-usable data science components that draw on software engineering best practice.
- Contribute technical expertise on collaborative projects with a thought-leadership, discovery, or strategic focus.
- Communicate project outcomes through visualization, written reports, demonstration and presentation.
Required skills and experience
- Degree in a numerate or scientific discipline (masters, PhD or equivalent industry experience preferable)
- Aware of a broad range of data science methods (for example a few from this list, or equivalent: classification, regression, clustering, network science, agent based modelling, bayesian modelling, gradient boosting, monte carlo methods, spatial methods, optimisation, forecasting). Experience quickly learning new methods in the context of a research or applied problem.
- Deep knowledge of theory and demonstrated applied knowledge of one or more advanced analytical methods of techniques (for example having undertaken a significant project in optimisation, deep learning, graph models or any other relevant domain).
- Day-to-day working knowledge of at least one programming language for interacting with data and designing, building and validating models and algorithms that go from data to insight. We mostly use python in the team. Willingness to pick up new technologies and languages.
- Familiarity with statistical and visualization packages (e.g. things like seaborn, sklearn in python).
- Good, clear and appropriate communication to both technical and non-technical audiences. Ability to adjust and respond to others with different perspectives.
If you would like to apply for this role, please email your CV and covering letter, quoting the relevant job role you are applying for, to: firstname.lastname@example.org