Experience

Applied experience across analytics, business analysis, operational reporting and engineering-led process improvement.

Professional & Applied Experience

Placemently

AI Data Analyst – Employment Placement

July 2026 - Present

Completing an applied fraud analytics placement covering data quality assessment, feature engineering, model evaluation, threshold selection and dashboard monitoring. The programme is strengthening my ability to translate technical findings into risk-based recommendations and clear business documentation.

  • Investigating fraud patterns, class imbalance and data-quality risks using Python.
  • Engineering and evaluating features to support predictive modelling.
  • Comparing model performance and decision thresholds against business costs.
  • Using AI-assisted workflows for research, code review, documentation and output validation.

Learning Support & Data Assistant

February 2024 – July 2026

Combined learning support with assessment, reporting and operational data responsibilities. Worked across inclusion, curriculum, examinations and teaching teams to turn raw information and stakeholder needs into structured trackers, summaries and practical next steps.

  • Built and maintained Excel-based trackers covering assessment, intervention and operational activity.
  • Supported NGRT, CAT4, examination and access-arrangement processes across multiple year groups.
  • Identified data-quality issues, exceptions and reporting gaps before communicating them to relevant stakeholders.
  • Produced progress summaries and decision-ready reporting for senior leaders and operational teams.

Industrial Student Assistant

November 2022 - May 2023

Designed and evaluated a manufacturing jig for specialist engineering components, applying engineering analysis, CAD and process-improvement thinking to develop a practical production solution.

  • Gathered technical and operational requirements from the project brief.
  • Developed and compared design concepts using Autodesk Inventor and engineering analysis.
  • Considered manufacturing cost, usability, safety and implementation constraints.
  • Produced a final recommendation intended to improve efficiency and reduce projected production costs.

Growth stage

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Midsized

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Enterprise

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Education

LSE

Data Analytics Career Accelerator Programme

2024 - 2025
  • Distinction: 74%
  • Employer Project: 81%
  • Applied Excel, SQL, Python, Tableau, statistical analysis and data storytelling to business-focused projects.

BEng Mechanical Engineering

2020 – 2023
  • Developed a strong quantitative and structured problem-solving foundation.
  • Applied CAD, modelling, engineering analysis and project delivery methods.

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Tools & Methods

  • Business Analysis:
  • Requirements Gathering
  • Stakeholder Engagement
  • Process Mapping
  • Root-Cause Analysis
  • Risk Analysis
  • Documentation
  • BI & Analytics:
  • SQL
  • Excel
  • Python
  • Tableau
  • KPI Design
  • Dashboarding
  • Data Quality
  • Statistical Analysis
  • AI-Enabled Delivery:
  • AI-Assisted Research
  • Documentation Support
  • Code Review & Debugging
  • Analytical Quality Assurance
  • Output Validation
View My Projects
Contact Me

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Buyer trends

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Marketing

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Overview

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We bring technology excellence to your business

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