50% FTE role with MSQ Data driving data informed growth, fame and pitch and project work across MSQ clients. Delivering data science and AI strategy, ML models and data engineering projects for clients.

Leading a team of 9 fabulous data architects, analysts, data scientists and optimisation specialists to improve the data available to businesses for digital marketing decisions. Driving cloud adoption to enable real time, end to end measurement supported by machine learning and advanced analysis.

Leading a team of 30 analysts across 6 teams and 8 sites with responsibility for estimating costs, savings and revenues on the £177bn student loan book. Key responsibilities include:• Leading a full stack data science unit to develop and assure of a suite of models (in R and SQL) forecasting current and future loan stock and the borrower journey.•Leading team forecasting returns to loan sale investors, ensuring compliance with ratings agency and auditor needs and managing relationships.• Advising ministers on evidence and analysis for decisions related to student finance, including uncertainties and risks.• Developing work plans which support short and long term business needs whilst identifying and managing risks to successful delivery• Ensuring the appropriate L&D, contingency, resourcing and handover plans are in place for continual smooth functioning of critical deliverables across the team,• Hiring, developing and mentoring team leaders and developing training resources for data scientistsTeam won the Government Operational Research Service Innovation Award 2022, for novel uses of machine learning to drive government decisions.

Leading a team of 7 analysts to estimate the government subsidy on student loans; provide analysis and advice on Higher Education policy development; and improve and maintain forecasting models in R and SQL.Key achievements included:• Providing key analysis and advice across government to support the reclassification of student loans to prevent fiscal illusions, worth £14bn.• Integrating new longitudinal administrative data sources into earnings forecasts and machine learning methods (K-nearest neighbour matching, cluster analysis and random forests) to improve accuracy and broaden forecast usage.

Leading a small team to deliver operational insight into the roll out of the Youth Obligation under Universal Credit, and insight for policy and the public into skills amongst young people.Key achievements included:• Creating analytics dashboards for operations to show bottlenecks and non-compliance with the benefit system, leading to a significantly improved customer journey for claimants• Working with DfE to create joint datasets which allowed continued publication of statistics on learning by claimants under Universal Credit.

Analyst on the civil service graduate learship scheme. Analysing the impact of government reforms on low income households (in SAS) and advising on the equalities impacts of benefit reform. Graduated from the four year leadership scheme in two years.

Novel research into behaviour of elastic composites containing hollow spheres taking into account interaction effects between spheres, using a mix of theoretical development and computer simulation. Included industrial secondment to New Zealand for experimental analysis.Well versed in mathematical modelling, programming in Mathematica and scientific writing, with excellent research and communication skills.

3 month fellowship at the House of Commons Library in the Science and Environment Section sponsored by EPSRC and POST. Provided the opportunity to use research skills honed throughout my PhD and to develop excellent communication skills through writing briefs and research papers for MPs on a wide variety of topics within science and technology.