At Oxfam our approach to public engagement is driven by our audiences. By understanding what motivates different people to support Oxfam, we hope to deliver the right content, through the right mediums at the right time to give our supporters a great experience. Here in the insights and intelligence team, we recognise how good quality data and evidence can drive strategies such as fundraising and brand. Our key functions and responsibilities include:- Business Intelligence and analytics- Market research and audience insight- CRM and data management- Data selections- Divisional performance monitoring and evaluation- Horizon scanning

Innovation and Insight is a growing directorate within the British Red Cross which aims to increase our understanding of how we can make the greatest impact when working with people who access our services, and connect this to frontline operational staff through innovation.Until January 2018, I led a team of 7 to become the insight team within this exciting directorate. As the first post holder for this role, I primarily focused on understanding the organisational priorities in order to set direction and build the team in line with the long term vision. Our core focus was to blend qualitative insights with hard data, through evaluation, co-production, data science and performance analytics - not only delivering projects within the team, but building and supporting the organisations capability to do this at every level.In 2017 we worked with all of our UK services to ensure their Theory of Change models are aligned to their reporting framework; whilst building momentum and excitement around foresight; co-design of services and other insight creation approaches.In February, since our function has continued to grow, my role was enlarged into Head of Business Analytics. This still includes our data science and foresight work, alongside feedback, data quality and a business intelligence team - seeking to develop the analytics and data science work into a team who can provide data driven insight at an enterprise level.

The Advanced Analytics Unit our mission was to transform the way NOMS provides data and insight within the Agency. Working closely with an exciting agenda of digital transformation, I led development of the unit in primarily two areas: Firstly, to develop an analytics infrastructure in partnership with our IT provider. This project will - for the first time in NOMS - enable near real-time insight generation and dissemination around the business. Secondly, to build the skills and visibility of the unit as we grow and expand. Through rapid development of exemplar projects which bring Advanced Analytics and Data Science together with digital, we are driving and shaping the department's Analytics strategy.

I led the Community Performance team: providing performance information and business intelligence on probation services to the agency and MoJ. We designed, tested and implemented a new end-to-end performance framework for probation services. Based on this, we delivered dashboards and ad-hoc performance analysis to senior NOMS and MoJ executives. These reports were used at board level and highlighted information on offender volumes within the system which directly triggered commercial action.I was responsible for provided these analyses for the NOMS managed National Probation Service as well as the contractually managed Community Rehabilitation Companies.As well as this, we provided forecast estimates of future volumes and payments internally, whilst supplying commercial colleagues with the data to support contractual payments. Our team was an integral part of the commercial function and built a strong reputation for insight and accuracy.I line managed 7 professional Operational Research/Statistics staff with 3 direct reports. I was responsible for statistical publications and ownership of the payment mechanism for Community Rehabilitation Companies worth over £500m per year. This payment mechanism was built by our team from scratch, and not once were model outputs challenged or queried for calculation accuracy by the providers.My team will also be the first point of contact for contract management colleagues who require analytical input on any community contracts covering various aspects of NOMS business - for example: electronic monitoring and prisoner escorts.

I delivered an automation system to streamline the collection, production and dissemination of HR management information. This increased processing efficiency during a time that the team was reducing in head count. Working with the MI strategy lead, I also used this piece of work to support design a new MI strategy for the business which would eliminate the burdensome manual elements of collating and distributing data.During this role I also sought many developmental opportunities, such as becoming a Science, Technology, Engineering & Maths (STEM) ambassador for schools, and delivered numerous presentations to students on the possible careers enabled by studying maths. I also developed a managed move process to enable GORS fast-stream analysts to manage their early career choices effectively.

I developed scenario modelling to support policy development in the Transforming Rehabilitation Programme. This model brought unit costings together with strategic choices on operating models to ensure the impact of strategic options was fully understood by policy colleagues. I was also part of the team who developed an offender segmentation model for NOMS. By utilising evidence and research from colleagues in NOMS, we created a robust way to segment offenders into groups with similar needs. This dataset became integral in how colleagues commissioned accredited programmes and other behavioural interventions across the business.

Continuing my role in the Census QA team, I picked up line management of 4 junior staff.I also coordinated the statistical commentary for the Census results, leading a team of statisticians in the development, quality assurance and description of statistical tables and outputs.

I designed, built and operated a SAS/Excel data architecture and data matching methodology for the quality assurance team of the 2011 Census. The SAS engine used probabilistic data matching techniques to identify likely matches. My excel front-end automatically presented these data to our clerical matching team in a user friendly format.