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- Tasks: Use data science to tackle homelessness and create impactful insights for London.
- Company: Join LOTI, a collaborative innovation team in local government.
- Benefits: Competitive salary, hybrid working, and opportunities for professional growth.
- Why this job: Make a real difference in people's lives while developing your data skills.
- Qualifications: Experience in data analysis, machine learning, and strong communication skills.
- Other info: Be part of a dynamic team focused on innovation and social impact.
The predicted salary is between 44000 - 62000 £ per year.
Responsible to: Data, Smart Cities & Cybersecurity Programme Manager
Starting salary: £52,344 per annum
Working Hours: 35 per week
Contract details: Fixed term contract (18 months). Full time. Monday to Friday. 9:00 am to 5 pm. Hybrid working, some mandatory office days required.
If you have any queries about the role, please contact us at: contact@loti.london.
For any issues with your application please contact recruitment@londoncouncils.gov.uk.
Application deadline: 11:59pm on (10 March)
Interviews: week commencing 16th March
We are looking for a Data Scientist to join London's mission to end homelessness. The Data Scientist will play a core role in the new Ending Homelessness Accelerator Programme (EHAP). You will generate insights, understand trends, test hypotheses, and inform policy design that will support London Boroughs in their efforts to make homelessness and rough sleeping rare, brief, and non-recurrent.
Working with partners across LOTI, London Councils, the Greater London Authority (GLA), London Housing Directors’ Group, Boroughs and the Centre for Homelessness Impact, you will use your skills to make a real impact in people’s lives.
Working alongside the Data Projects Manager, the Data Scientist will support the programme to answer critical questions about homelessness in London by:
- Using machine learning and advanced analytics to identify the causes of homelessness, helping boroughs intervene before a crisis occurs.
- Building engagement and understanding of homelessness and rough sleeping data across the boroughs.
- Developing the data collection and analysis capability of the programme and its partners.
- Translating complex statistical models and their outputs into Data Stories and interactive dashboards that Housing Directors can use to inform policy and commissioning decisions.
- Experimenting with innovative analytical methods to improve the efficiency and effectiveness of homelessness data analysis and sharing lessons learned.
Role Responsibility
Analysis and Insight Generation
- Select and apply appropriate and innovative analytical techniques to data and synthesise findings, specifically focusing on homelessness and rough sleeping datasets, including the Rough Sleeping Insights Tool, Temporary Accommodation, Inter-Borough Accommodation Agreement and more.
- Work as part of a multidisciplinary team, taking responsibility for analysis undertaken in response to outcomes and requirements identified by the team.
- Develop basic forecasting and predictive models to anticipate trends in homelessness and rough sleeping, helping to inform proactive policy responses.
- Interpret and identify patterns in data for a range of audiences, helping them understand potential conclusions and opportunities, and suggesting next steps for policy and intervention.
- Experiment with innovative methods to conduct analysis and share lessons learned with analysts in partner organisations to improve the efficiency and effectiveness of homelessness data analysis.
- Proactively ensure that data collection and analysis is organisationally effective and duplication is minimised.
Data Handling, Quality and Visualisation
- Prepare and cleanse data with experience at data cleaning and preprocessing techniques such as removing duplicates, handling missing data, and data normalisation, ensuring its accuracy and fitness for purpose.
- Use data visualisation tools to create visuals from complex datasets, telling compelling stories that are relevant to policy goals and can be acted upon by stakeholders.
Collaboration and Communication
- Effectively communicate with technical and non-technical stakeholders, including tailoring communication to specific audiences.
- Proactively seek perspectives from non-analytical peers including subject matter experts and frontline workers to support projects and gain richer user insights.
- Manage the production of best practice guides for data analysis within the team.
- Proactively build relationships that enable effective cross organisation collaboration within the Pan-London team and with external partners.
- Work with the Data Projects Manager to identify user requirements and feed these into future feature and functionality development.
- Actively seek out new and insightful ways to present and visualise statistical data related to homelessness and rough sleeping to boost user engagement and policy impact.
The Ideal Candidate
Person Specification:
- Ability to understand business and policy problems and to address them using data from multiple sources.
- Experience using predictive, statistical, or other mathematical techniques including supervised and unsupervised machine learning (including the ability to determine the best technique to solve a particular problem).
- An excellent grasp of standard statistical techniques for data analysis and exploration, such as regression and cluster analysis, and as well as experience using these techniques to solve real-world problems in a work environment.
- Strong proficiency in applying statistical techniques and machine learning algorithms using a variety of software/codebases e.g. R, Python to build reproducible processes.
- Ability to identify and effectively communicate data stories using data visualisation techniques with a range of audiences.
- Ability to quickly research and learn new programming/modelling tools and techniques.
- Ability to extract, clean, link, enhance and model data sets in a variety of software packages in a timely, effective and clear way.
- Experience visualising data sets through modern tools such as R, Python, PowerBI, Tableau etc.
- Experience/knowledge about infrastructure for big data and data science analysis.
- Experience of taking ownership and responsibility for your work, prioritising and organising work effectively and to operate as part of a team.
- A postgraduate degree in a quantitative field strongly related to data science, i.e. one that involves applied mathematics/statistics and coding or equivalent professional experience.
- Care about achieving outcomes that support innovation and benefit London and its population.
- Enjoy problem-solving in new, complex and sometimes ambiguous environments where both creativity and pragmatism are required.
Desirable
- Expertise in and experience of working with Homelessness Data.
- Understanding of information governance principles and data protection legislation.
- Knowledge of modern data management practices, including the technologies used, platforms and services.
- Ability to think creatively about the use and meaning of data patterns and insights.
- Experience of delivering insights to senior stakeholders and helping them to understand them.
About LOTI
LOTI is London local government’s collaborative innovation team. We help London borough councils and the Greater London Authority (GLA) use innovation, data and technology to be high performing organisations, improve services and tackle London’s biggest challenges together. We connect more than 1,500 local government colleagues online and in person, helping them to share knowledge, build capacity, run projects and influence change together.
How we work
The LOTI team is currently made up of 10 people. While being small, LOTI can draw on the time, ideas and energy of dozens of people from across our membership. We are committed to being a high-performing team, constantly reviewing, learning and adapting our ways of working, and operating in a high-challenge, high-support culture. We approach all aspects of our work with an outcomes-driven mindset, with an emphasis on finding creative and pragmatic solutions.
Data Scientist employer: London Councils Group
Contact Detail:
London Councils Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for interviews by practising common data science questions and case studies. Use platforms like StudySmarter to brush up on your skills and get comfortable explaining your thought process and methodologies.
✨Tip Number 3
Showcase your work! Create a portfolio of projects that highlight your data analysis and visualisation skills. This will give potential employers a taste of what you can do and how you approach problem-solving.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in being part of our mission to tackle homelessness in London.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with data science, especially in relation to homelessness. We want to see how your skills can directly contribute to our mission!
Showcase Your Skills: Don’t just list your technical skills; demonstrate them! Include examples of projects where you’ve used machine learning or data visualisation tools. We love seeing how you’ve made an impact with your work.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon where possible. We appreciate a well-structured application that gets straight to the point!
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Don’t miss out on this opportunity!
How to prepare for a job interview at London Councils Group
✨Know Your Data
Before the interview, brush up on your knowledge of homelessness data and the specific datasets mentioned in the job description. Familiarise yourself with tools like R and Python, and be ready to discuss how you've used them in past projects.
✨Showcase Your Analytical Skills
Prepare to discuss your experience with predictive modelling and statistical techniques. Think of examples where you've applied machine learning to solve real-world problems, especially in a collaborative environment.
✨Communicate Effectively
Practice explaining complex data insights in simple terms. You might be asked to present a data story, so think about how you can tailor your communication for both technical and non-technical audiences.
✨Engage with the Mission
Demonstrate your passion for tackling homelessness in London. Research LOTI's initiatives and be prepared to discuss how your skills can contribute to their mission of making homelessness rare and brief.