Leading the data science team and co-leading the AI department, where I performance manage and mentor a large team of data engineers and scientists. Our team operates across both public and private sectors, specialising in delivering high-quality data solutions to our clients

(LV= General Insurance became Allianz Personal)Line manager for 4 direct reports (1 Lead, 2 Senior, 1 Mid-level)Part of a 60 person team of Data Scientists, Data Engineers and Data Hunters with 80+ models in productionProduct Owner for Lifetime Value Optimisation Pricing Data Science- Delivered a suite of machine learning based pricing models (Conversion, Retention, Payment Method & Addons) for optimising 3 year customer lifetime value- Delivered a gamma distributed regression model to predict the average of the top 5 cheapest quotes a potential policyholder would see on a price comparison website. Built with iterative development practices, interactive model evaluation reports for stakeholders, cross validation for stable future retraining and automated data extract creation.Led the development of a model/process to identify potential large loss(100k GBP) claims and route them to specialist teams - using NLP, surge event indicators and claim metadataLeading MLOps projects for automation in data science including CICD, improved logging & monitoring and memory/time bottleneck identificationWon internal team awards for collaboration and proactivity

LV General Insurance became Allianz PersonalDeveloped and deployed a model to evaluate customer retention based on different price points - as part of LV's mission to implement dynamic insurance pricing and new Financial Conduct Authority RegulationLine management for one direct report and mentoring of junior data scientists in clean code and softwareDevelopment of a production python package for generalised regression intervals Application of generalised regression intervals to frequency & severity models for insurance pricingBuilding on existing best practices on production and analysis code review

OnCorps helps people make better decisions by giving them personalized decision making tools powered by machine learning and behavioural science algorithms. The system makes recommendations by learning from the successes of peers and the behaviours of each user. The system learns and adapts with each interaction: recording better data, providing feedback, measuring reactions, and adjusting recommendations.Notable achievements:- Development of two models to determine win probability of potential pursuits for a large technology consulting firm. A static classification model to forecast which pursuits will win and a multi state survival model to calculate the probability of a pursuit moving through different stages of the sales process within a certain number of days.- Building Data Science environments with Docker to deploy models and graphics as APIs to other applications- Upskilling the Data Science team in the model building process and model deployment architecture- DS/ML Solution Design inc. Architecture, Business Problem definition.- Creation of the remote multi-user data science training environment running JupyterHub and Rstudio Server- Design and implementation of a tax news recommendation engine for a Big 4 Accounting client- Administrator of the Databricks platform + associated spark clusters

Providing Data Consulting, Machine Learning Models and Simulation Modelling for clients in Banking, Government & HealthcareCompetition for Large Pharma Client - Transformation, Exploratory Data Analysis and Feature Engineering of SAP accounting data for a model to predict net sales for drugs in certain time frames. Extracted features from Pubmed and FOREXMajor European Lottery provider - Calculated expected distributions of lottery payouts and accounting variance (via a Binomial Distribution) under certain promotion/prize conditions in Python (pyspark and scipy)Transport Client - Consulting on passenger counting IOT devices and conducting an Exploratory Data Analysis in tidyverse R and Amazon Redshift SQL (also using OpenStreetMaps) to determine this company’s readiness to implement Machine Learning and ‘Industry 4.0’. Awarded KPMG Operations Consulting Project of 2017Mathematical Simulation Model Platform - Lead Mathematician and R Developer for a consulting sales software based on network analytics and Fuzzy Cognitive Maps built with R (igraph, parallel, tidyverse, jsonlite) and mongoDB/CosmosDB, hosted on Microsoft Azure. Developed using R's High Performance Computing techniques.Major UK Bank - Automation of Indicia Review for Common Reporting Standard/Foreign Account Tax Compliance Act (via Random Forest Classifier) built in PythonUK Government - Developed and implemented a Power BI reporting solution for a major recruitment project at a Government Ministry. Advised Senior Civil Servants and the Secretary of State on the recruitment process using this reporting. Both Recruitment Targets achieved.NHS Trusts - Working with data on turnaround times, HRGs, payroll and bed modelling

Lead and coordinated the following projects:- Effective Communication with Primary Care: My role focused on reconciling differences between providers’ and commissioners’ findings and perspectives. I advised and assisted in the implementation of changes to data quality and Clinical Letter and Electronic Discharge Summary production practice, in order to assist this NHS Trust in achieving its £350,000 contract target. This involved working closely with IT, Informatics, clinical/administration teams and the project management office. - Automated Communication: Investigating the effectiveness of the current automated phone and call service on patient attendance. This involved all aspects of project management including scoping the subject, analysing data and developing/implementing project plans.