United Kingdom
I'm a Business Analyst who builds AI and automation systems for regulated environments, where "we can't use AI because of compliance" is usually the real blocker, not lack of use cases. 🤖 AI Engineering for Regulated Data Built an AI-powered proofing tool at Civica that tests every permutation of variable data fields (name, address, account number) across live production runs, using an anonymisation layer so the AI never sees sensitive data, and optimised token usage to keep the process fast and cost-efficient at scale. This solves the exact confidentiality barrier that stops most regulated organisations from adopting AI-driven process automation at all. 🛠️ LLM & Automation Tooling Building a growing portfolio of AI tools for wider business use, chatbots, automated reporting, knowledge bank systems, designed to be reusable across teams, not one-off scripts. Improved chatbot response accuracy by 20% on Civica's Polling Station AI assistant through training data preparation and validation. 🔒 Compliance-First Approach 5+ years working inside regulated, high-stakes environments (energy utilities, local government) where I've had to design data governance frameworks that satisfy internal risk policies by default, not as an afterthought. That discipline now shapes how I approach process re-engineering and digital transformation: anonymisation before automation, validation before deployment. Strong foundation in stakeholder management and requirements gathering across cross-functional and Board-level audiences. 📊 Proven Process Impact Lifted billing accuracy from 24% to 67% across a 3.5M-customer dataset at K-Electric by tracing errors back to root cause and redesigning validation logic, work that went to the Board and secured approval for a large-scale workforce optimisation initiative. 🎓 Technical Foundation SQL, Python, Power BI, Business Intelligence, LLM-based automation and prompt/token optimisation, alongside an MSc in Business Analytics (Warwick Business School) and CFA Level 1. Currently looking at Business Analyst roles focused on AI-driven process automation in regulated or compliance-heavy sectors, where getting the AI-and-confidentiality balance right is the actual job, not a side skill. Let's connect 👋
• Enabled clients to safely sign off final communications to thousands of recipients by building an AI-powered proofing tool that tests all permutations of variable data fields (name, address, account number), using an anonymisation layer and optimised token usage to prevent errors that risk thousands of pounds in postage costs and GDPR breaches. • Expanding AI capability across the wider business by building a portfolio of work-specific tools, including chatbots, automated reporting, and knowledge bank systems, for reuse by employees across multiple teams. • Improved chatbot response accuracy by 20% for Civica's Polling Station AI assistant by preparing and validating training datasets used in model output. • Cut manual effort by 40% across 3 operational teams by designing automated workflows for regulatory reporting, mailing, and client communications with logic-based segmentation and compliance checks. • Reduced document errors by 35% on 100k+ monthly client communications by building automation solutions in Python, VBA, and Quadient Inspire with exception-handling logic. • Resolved 10+ recurring issues at source by tracing data quality exceptions back to their root cause using structured exception logs and pattern analysis across reporting cycles. • Improved data accuracy by 25% across 3+ major reporting workflows by consolidating and validating multi-source datasets using SQL, Excel, and Microsoft Access. • Delivered 100% of client projects on time by coordinating across clients, developers, and production teams to translate technical data into stakeholder-ready insights.
• Increased billing accuracy from 24% to 67% across a 3.5M-customer dataset by implementing SAP IS-U elasticity tolerance checks, consumption data analytics, anomaly detection, and statistical validation. • Reduced billing anomalies by 15% and strengthened data quality governance by applying A/B testing to optimise tolerance thresholds and validation rules across the billing system. • Secured Board approval for a large-scale workforce optimisation initiative by presenting data-driven forecasting analysis and recommendations directly to executive leadership. • Recommended expanding field service capacity by 62% (789 to 1,280 teams) to hit a 70% service-level target over 5 years by building forecasting models and sensitivity analyses on fault volume trends. • Cut workforce planning time by 30% across geographically distributed service areas by building a Corrective Maintenance capacity planning model to determine optimal field team allocation. • Established a 10% service-level baseline (up from no formal baseline) across 9 operational clusters serving 3.5M+ customers by analysing fault-resolution data and fault-type TAT metrics. • Reduced manual reporting time by 25% by designing Business Intelligence dashboards in QlikView, giving leadership real-time visibility into service KPIs, fault trends, and workforce utilisation. • Delivered a 5-month Billing Governance Framework rollout by leading UAT, stakeholder engagement, and change management across IT and business teams.
• Improved decision-making efficiency for procurement teams by designing 12+ interactive scorecard dashboard filters, enabling instant supplier comparison across multiple industries instead of manual side-by-side review. • Enabled smarter, faster sourcing decisions by building data-driven visual analytics on TDAP's exporter platform, giving procurement teams direct visibility into exporter performance and strategic fit. • Increased dashboard reliability and stakeholder trust in the data by mapping, cleaning, and standardising industry and supplier datasets before integration, eliminating inconsistencies that would have skewed benchmarking. • Surfaced high-performing partners and flagged risk areas by running trend analysis and KPI monitoring across supplier performance metrics, turning raw data into actionable procurement strategy. • Cut manual reporting effort by 30% and enabled real-time dashboard updates by automating data aggregation and reporting workflows using Excel and SQL, replacing a manual refresh process with a repeatable, self-updating one.
• Enhanced digital engagement with healthcare professionals by preparing E-detailers for 3 drug portfolios (Tazocine, Tygacil, Meropenem) for deployment across DigiTrends and Your Health Matters platforms. • Supported evidence-based antimicrobial stewardship decisions by contributing to the AMR-AMS project in collaboration with the World Health Organization and MMIDSP. • Enabled data-driven market strategy by building Primary and Derived Markets for multiple drug categories, filtering and analysing large-scale datasets from IQVIA and other healthcare databases. • Identified growth opportunities and supported portfolio optimisation by conducting market trend analysis and segmentation across drug categories. • Improved visibility for leadership and cross-functional teams by developing dashboards and reports summarising market share, prescribing trends, and competitor insights.
• Analyzed 559 companies listed on the Pakistan Stock Exchange (PSX) to evaluate creditworthiness, identifying 110 companies with acceptable credit risk for collateral eligibility. • Conducted scenario analysis and stress testing on financial data to finalize eligible companies, ensuring risk mitigation and compliance with FAS project guidelines. • Developed data-driven insights and reports to support decision-making for collateral management, improving accuracy and transparency in financial risk assessment. • Streamlined analysis workflows using Excel and SQL, reducing manual processing time and enabling faster evaluation of large datasets.