Christopher Boughton

Accomplished senior data scientist with 6 years of professional experience contributing to outstanding success across both science and financial fields. Confident in ability to develop production-grade modular applications using the golem framework and transfer and apply my skillset to new challenges. Comfortable leading teams, collaborating with others and working independently in a variety of settings including SCRUM sprints and GitHub-based DevOps.

Have developed a number of key skills through my professional career including: Project Management

• Team Leadership
• R/R Shiny/R Markdown
• Golem framework
• RDBMS (PostgreSQL)
• GitHub DevOps
• JIRA/Confluence Software
• SCRUM/Agile Leadership
• R6 Classes
• SQL Queries
• Linux
• Data Visualisation
• CSS
• HTML
• ML Techniques
• Stakeholder Engagement
• Non-Technical Communication
• Documentation
• KPI Tracking

• Lead the Data Science team and implemented Agile/SCRUM project leadership, GitHub-based DevOps workflows and JIRA/Confluence task orchestration to improve development workflows.
• Designed and deployed a production-grade modular RShiny application automating large-scale financial audit pipelines using the golem framework. Delivered end-to-end automation in ETL, statistical sampling, anomaly detection and risk scoring, reducing audit processing time from 10 days to 1 day. 
• Engineered a workforce optimization platform (RShiny + visually engaging interactive Plotly dashboards) applying scenario modelling and what-if analysis to predict staffing requirements, balance workload peaks and optimize resource allocation. Operational resourcing team routinely use the tool as the main rationale and predictor for resource planning, allocation and scheduling.
• Architected and built a PostgreSQL data warehouse including schema design, normalization and automated ETL pipelines, enabling seamless integration with deployed analytical applications. 
• Created advanced SQL queries and data extraction pipelines to deployed RShiny models and dashboards.
• Implemented machine learning algorithms (linear/logistic regression, unsupervised ML such as isolation forests) to generate predictive insights for audit analytics and fraud detection.
• Lead talent acquisition, onboarding and technical upskilling of junior data scientists, fostering a high-performing, future-ready team.
• Acted as a bridge between technical and non-technical stakeholders, delivering KPI-driven analytics, public data scrutiny and executive-level insights using Rmarkdown.
• Maintain Linux virtual machines, SSL certificates, configuration files and permissions through SSH.
• Authored business cases and procurement strategies for required resources such as Posit Licenses.
• Conduct code reviews and refactoring across R, SQL and RShiny projects to enforce best practices in software engineering, ensure maintainability and drive continuous improvement.

• Achieved advanced management certificate for demonstrating a variety of methods and strategies to produce high performing teams and deliver successful projects at pace.
• Developed skills and methods to effectively manage and lead teams (e.g. Prince2 and Agile).

• Delivered a high-impact RShiny audit automation tool, leveraging custom HTML/CSS/JavaScript integration to enhance UI/UX, navigation flows, and end-user adoption.
• Automated workforce analytics pipelines in R, generating actionable insights on staff availability, capacity planning, and operational forecasting for executive decision-making.
• Developed and productionized compliance-focused R scripts, enhancing adaptability to evolving legislation and improving accuracy of financial audits through packages such as tidyverse, lubridate, ggplot2, plotly, data.table, reactable and caret.

PhD in Veterinary Parasitology

• Directed current research at the 50-year long-term slurry site at AFBI Hillsborough. Provided statistical, analytical and visualisation support (R), resulting in ongoing collaboration through data consultancy.
• Developed a wide range of data proficiencies including data ingestion and manipulation (including large 50-year datasets), normality testing, data presentation, regression, linear models, GLMs, GLMMs, exploratory analysis such as PCA and many other statistical tests.
• Modelled (R) how climate influences ecological services through glasshouse experiments.