About Finn
I work with senior organisational leaders (typically board level or C-suite) to design and deliver data-driven strategic business transformation across the enterprise, involving team-building, prioritisation and project planning, programme leadership and delivery. I enable companies to deliver high-quality, scalable, production-grade technological solutions to their most challenging business problems.
I have over a decade of experience working with and engaging staff at all levels at all levels (from frontline up to senior leadership/C-suite) in data-intensive environments in both the public sector and industry.
I hold a masters degree in Computer Science (specialisation in financial computing) and have experience working across multiple sectors, with particular expertise in retail/consumer, government and financial services (counter-fraud analytics, risk, asset management and markets). I have been published in several industry publications.
I am available for technically expert senior-level roles, requiring strategic leadership and project management, including interim or fractional CIO/CTO/CDO positions
English
Native or bilingual
Can work on-site
Sturminster Newton (up to 50km)
Experience
- Whitehat Analytics LtdDirector of Data ScienceNovember 2017 - September 2021 (3 years and 10 months)• Grew a data science and advanced analytics consulting business from 1 to 12+ FT employees, annualised revenues from £100k to ~£1m+, winning new business and leading projects.• Defined business strategy and commercial decision-making.• Prospect engagement with C-level executives at large enterprises.• Programme management and delivery – managed data science project teams and provided technical advice to senior leaders.• Thought leadership and customer insight (e.g. website, blogs, white papers). Published on Datanami and Towards Data Science, popular data and analytics community websites, and interviewed for EM360 Tech podcast on data culture.Planned and delivered the recruitment and continuing professional development (CPD) strategies across multiple functional areas in the data and analytics space.• Example Project (May 2018 – Aug 2019): Led a programme of work to assist a Big Six energy supplier business to build a cross-functional data science team from scratch to over three dozen people. Delivered data-driven solutions, enabling understanding of the customer journey and improvement of operational performance and resilience. Provided technical expertise at all levels from strategic (direction setting, technical requirements and prioritisation) to tactical (solution implementation).o Built bleeding edge predictive models of the customer journey, incorporating all-source data (telephony, live chat interaction data alongside static customer characteristics data, such as location, age, house type and product type). Designed an NLP model for topic identification on phone call and chatbot transcripts and built an LSTM-based neural network to derive customer lifetime value (CLV) models and predict events such as customer churn.o Built ELT pipelines to create strategic data assets by integrating large (multi-billion row, multi-TB) financial, operational and transactional (e.g. clickstream) datasets, from diverse sources and formats (principally structured, free text and audio data) on AWS cloud using Python, Glue data catalogue, Athena (Presto) and EMR/Spark.o Software engineering using Cloud9 and the Lex framework to build a speech analytics pipeline for audio data.o Built advanced machine learning models on top of these data assets to deliver value via analytical insights, proofs of concept and visualisations across the business, from front line managers to C-level stakeholders, using Python and Tableau.o Provided technical expertise during the recruiting process for roles on the team.o Advised on appropriate database schemas, software and technical implementation of analytical solutions for a cloud-based data lake.o Advised more junior data scientists on problem setting, guiding selection of an appropriate approach and suggested details of algorithmic implementations.
- Department for Work and PensionsLead Data ArchitectJanuary 2017 - November 2017 (10 months)• Design and build of counter-fraud analytics algorithms and infrastructure• Manipulation, integration, ETL and matching of large (multi-TB), diverse data sets of structured and unstructured data from over two dozen diverse sources in JSON, XML and flat file/xsv, using SQL, Spark, Hadoop and bash.• Advised on the design and re-hosting of the analytical environment to an AWS cloud solution using EMR, Lambdas, and Elastic Beanstalk to handle high-throughput data analytics, secure hosting, and app deployment.• Planned redesign to build data science capability into hub business processes to improve results and enhance scalability.• Process automation using Python and bash scripting/cron scheduling• Data quality monitoring, resolution of issues or escalation to senior management• Senior stakeholder engagement to communicate the team’s key findings and forward outlook
- Department for Work and PensionsLead Data ScientistAugust 2015 - January 2017 (1 year and 5 months)• Led and worked within several cross functional agile project teams in newly established Data Science initiative focusing on leveraging the department’s data to improving operations.• Assisted in the technical design, user research and testing of the data science team’s AWS platform, software stack and infrastructure.• Using supervised and unsupervised machine learning and data mining techniques, conducted data analytics, produced software and visualisations in several high-profile data science projects for senior management, using Python and R.• Routinely engaged with senior stakeholders to translate business challenges into technology solutions.• Built visualisations in Django and D3.js to present results to senior departmental management and influence policy and operational decisions.
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Education
- Computer Science - Financial ComputingUniversity College London2014Dissertation/principal research project (with Distinction): Developed an algorithm to computationally model and mathematically optimise supply chain disruption risk for multi-national businesses, using linear programming techniques, in Java. Used industry standard solvers, including CPLEX and Gurobi, in partnership with Dun & Bradstreet, a leading corporate information and risk management consultancy. Supervisor: Dr. Daniel Hulme Example project (with Distinction): Led a team in a software engineering, design and programming project to develop an Android phone application. Undertook all project data modeling and handling, system design, writing database management interfaces, and software testing/validation.
Skill set (17)
Categories
- Tech Executive