Phase Innovate is an Irish non-profit empowering underrepresented communities — women, youth, migrants and marginalised groups — through technology education, entrepreneurship and STEM outreach. Partners include AWS, PwC, PayPal and Microsoft. Tech team of 34 engineers, one Tech Lead and one CTO.

• Conducted user research with underrepresented communities in tech — user interviews, focus groups, surveys and usability testing.
• Designed accessible, inclusive interfaces for digital platforms, learning tools and event management systems.
• Built and tested prototypes iteratively, reporting findings to stakeholders and informing design decisions.
• Produced research reports sharing insights with programme teams to guide future development.
• Evaluated programme effectiveness in addressing gender and racial gaps in tech access.
• Collaborated with developers, educators and stakeholders to integrate user-centred design across all initiatives.
• Contributed to community events and workshops, sharing UX knowledge and raising awareness of the organisation's mission.
• Delivered 15+ documented UX improvements directly attributed to research and testing cycles.

End-to-end UX research, design and testing for an independent London fashion e-commerce business. Full project available on portfolio. (Business has since retired.)

• Conducted discovery research to understand target audience needs, business goals and user pain points across a multi-product platform.
• Facilitated usability testing workshops; reported a 100% improvement in user experience journey quality.
• Created a complete brand identity from scratch — logo, colour palette, typography and iconography.
• Redesigned information architecture, user journeys and interactive UI across all platform pages.
• Built and launched the full Shopify store; validated complete user journey from browsing to payment.
• Increased site views and engagement by 90% following relaunch; significantly reduced bounce rates.

End-to-end design, build and evaluation of an environmental risk prediction system — classification and regression ML pipelines wrapped in an accessible, interactive web dashboard. Project IP protected — high-level approach below.

• Built classification and regression ML pipelines using real-world hydrological and environmental monitoring data.
• Implemented end-to-end ML lifecycle: automated data ingestion via API, data cleaning, feature engineering, model training, evaluation, and deployment as a live web application.
• Applied CRISP-DM methodology with emphasis on reproducibility, versioning, and model evaluation across multiple feature sets.
• Built an interactive dashboard for real-time risk prediction and scenario testing via user input.

Tools: Python · scikit-learn · Streamlit · Plotly · Figma · GitHub