Data Scientist (Part-time)

Role overview

• check_circle Support and shape a new policy and regulatory function within the GLA
• check_circle Drive innovation in how licensing supports London’s economic and cultural vibrancy
• check_circle Work with senior stakeholders across government, industry, and the city
• check_circle Make a visible impact on how London works, plays, and grows
• check_circle Using data science techniques to generate insights that support the design and delivery of London’s new Strategic Licensing powers
• check_circle Working closely with policy leads, analysts and external partners to translate complex questions into clear, actionable data projects
• check_circle Analysing large, complex and multi-source datasets (including time-series data) to understand trends in London’s licensing, cultural and night-time economy
• check_circle Developing and maintaining reproducible data pipelines, ensuring high-quality, regularly updated analysis that informs decision-making
• check_circle Applying statistical modelling and machine learning techniques to evaluate the impact of the Strategic Licensing pilot
• check_circle Exploring and experimenting with new datasets, tools and methods to improve how licensing data is used across the GLA
• check_circle Creating compelling data visualisations and communicating findings clearly to both technical and non-technical audiences
• check_circle Collaborating with London boroughs, consultants and industry stakeholders to innovate in the use of data across the licensing system
• check_circle Contributing to London’s open data community by publishing datasets, sharing insights and promoting best practice
• check_circle Supporting and mentoring colleagues by sharing knowledge on coding, data science tools and analytical techniques
• check_circle Participating in cross-functional project teams, helping to shape a high-profile, city-wide policy programme
• check_circle Ability to understand business and policy problems and to address them using data characterised by high-volume, high dimensionality and from multiple sources – particularly time-series data.
• check_circle 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).
• check_circle 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.
• check_circle Strong proficiency in applying statistical techniques and machine learning algorithms using a variety of software/codebases e.g. R, GIS, Python to build reproducible processes.
• check_circle Ability to identify and effectively communicate data stories using data visualisation techniques and software, and quickly research and learn new programming/modelling tools and techniques
• check_circle 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.
• check_circle Listens to understand requirements without making assumptions
• check_circle Demonstrates an enthusiastic and ‘can do attitude’ to all requests
• check_circle Provides timely, accurate and personalised responses
• check_circle Provides a polite and helpful first point of contact for stakeholders
• check_circle Learns from feedback to improve personal service to others
• check_circle Prioritises work in line with key team or project deliverables
• check_circle Makes contingency plans to account for changing work priorities, deadlines
• check_circle and milestones
• check_circle Identifies and consults with sponsors or stakeholders in planning work
• check_circle Pays close attention to detail, ensuring team’s work is delivered to a high
• check_circle standard
• check_circle Negotiates realistic timescales for work delivery, ensuring team deliverables can be met
• check_circle Processes and distils a variety of information to understand a problem fully
• check_circle Proposes options for solutions to presented problems
• check_circle Builds on the ideas of others to encourage creative problem solving
• check_circle Thinks laterally about own work, considering different ways to approach problems
• check_circle Seeks the opinions and experiences of others to understand different approaches to problem solving
• check_circle Proactively seeks new information sources to progress research agendas
• check_circle and address gaps in knowledge
• check_circle Grasps limitations of or assumptions behind data sources, disregarding
• check_circle those that lack quality
• check_circle Analyses and integrates qualitative and quantitative data to find new insights
• check_circle Translates research outcomes into concise, meaningful reports
• check_circle Identifies relevant and practical research questions for the future
• check_circle Up to date CV
• check_circle Personal statement with a maximum of 1500 words. Please ensure you address how you demonstrate the essential criteria outlined above in the advert.
• check_circle If the number of applications received is high, we will review based on Research and Analysis Competency.

Benefits

• check_circle On a permanent contract or a fixed term contract where the time left on the contract at the point at which you apply for secondment is longer than the secondment period;
• check_circle successfully completed your probation period and are performing to the required standard for your current role; and
• check_circle at least 18 months service with the GLA

About the company