ABOUT ME
Making complex systems work for people.
I’m Praise Kapota, a University of Southampton Computer Science graduate and Apple Product Zone Specialist.
I work across product, data and customer experience. Since joining Apple on 1 June 2024, I have raised tool adoption, performed above store benchmarks and converted business opportunities into active accounts. My 2:1 degree sharpened the same instinct I use at work: define the problem, test the evidence and turn what I learn into a decision people can act on.
At a glance
02 / EXPERIENCE
A record of turning friction into progress.
Product Zone Specialist
I combine technical guidance, commercial judgement and customer insight in a high-volume environment, then use tracked performance to improve how I work.
- Awarded Exceeding Expectations in the Results category of my most recent annual review, recognising my contribution to increased utilisation of an internal tool.
- Diagnosed why adoption was stuck below 20%, changed how the tool was introduced and tracked take-up weekly. Usage reached around 70%, including 100% across the September 2026 launch weekend.
- Converted 80% of business-customer introductions into active accounts through the September 2026 quarter. Business accounts contributed 30% of store revenue during that measured period.
- Maintained an NPS of 89 against a store average of 85, Connect and Personalise of 95%, and TMS of 90–100% across consecutive quarters through September 2026.
- Handle account setup, VAT invoicing and confidential commercial records under UK GDPR and Apple’s Business Conduct policy, while explaining technical choices at the level each customer needs.
Product Manager
Ran a five-month software delivery with a six-person team of engineers and designers, holding the release date through three scope changes.
- Set priorities, translated user needs into buildable journeys and decided which features made the release when time and effort competed.
- Owned the backlog and delivery schedule, ran sprint ceremonies and kept risks, dependencies and decisions visible to the team and stakeholders.
- Used retrospectives as a measured feedback loop: identify the largest constraint, change one thing and check whether the following sprint improved.
Student Representative
Elected to represent more than 200 students, turning individual concerns into ranked, evidence-backed recommendations for senior faculty.
- Gathered qualitative feedback, identified recurring themes and presented practical proposals with enough evidence to support a decision.
- Contributed to two curriculum changes and one timetabling change adopted by the department.
03 / CAPABILITIES
From an unclear need to a useful outcome.
Choose a capability to see the evidence behind it.
Turned user needs into buildable journeys, backlog items and release decisions for a five-month software delivery.
Owned the backlog and delivery rhythm for a six-person team, using short iterations and retrospectives to improve the next sprint.
Planned sprint scope, kept progress visible and used reviews to align stakeholders on what was ready and what came next.
Balanced value, effort and timing to hold the release date through three scope changes.
Listen for the underlying need, test assumptions and translate what I learn into practical journeys or recommendations.
Kept risks, dependencies and decisions visible during delivery and presented evidence-backed proposals to senior faculty.
Worked across engineering and design in a six-person software team, keeping shared outcomes ahead of individual disciplines.
Combined feedback themes with tracked measures, from representing 200+ students to controlled dissertation testing and weekly adoption data.
Adapt technical explanations to each customer and maintained an NPS of 89 against a store average of 85.
Diagnosed why internal-tool usage was below 20%, changed the introduction and supported colleagues as adoption rose to around 70%.
Consider readable structure, keyboard access, reduced motion and clear language when shaping digital experiences.
Built an end-to-end object detection and tracking pipeline and evaluated machine-learning models across multiple configurations.
Designed relational schemas and wrote multi-table queries using joins and aggregates, checking results against source records.
Apply modular thinking, version control, testing and documentation across coursework and independent technical work.
Use visual flows and prototypes to make journeys tangible, gather feedback and reduce ambiguity before build work begins.
Use Excel to organise and analyse performance data, and PowerPoint to communicate coursework and stakeholder proposals clearly.
Use Claude and ChatGPT for coding, research and document workflows, checking outputs against sources before relying on them.
Use version control to make technical work traceable, reversible and easier to collaborate on.
PROJECT AND PRODUCT DELIVERY
How I move a product from idea to release.
I led a five-month software delivery through this cycle. Stakeholder communication runs through every stage. After release the loop starts again with what users tell us.
Understand the problem before choosing a solution.
In practice Translated user needs into journeys the team could build.
Decide what matters most when time and effort compete.
In practice Chose which features made the release. The date held through three scope changes.
Work in short iterations with progress kept visible.
In practice Owned the backlog and schedule and ran sprint ceremonies for a six-person team.
Measure the result and change one thing at a time.
In practice Each retrospective found the largest constraint. The next sprint showed whether the change worked.
Keep stakeholders aligned on risks and decisions.
In practice Kept risks and dependencies visible to the team and stakeholders alongside every decision.
TECHNICAL EVIDENCE
I changed the hypothesis when the evidence changed.
For my dissertation, I took an object detection and tracking system from question to evaluation in Python. I surveyed machine-learning approaches, implemented a classical computer-vision pipeline, prepared the dataset and ran controlled comparisons against accuracy metrics. When the results contradicted my initial assumption, I dropped the assumption rather than forcing the conclusion.
- End-to-end Python implementation
- Dataset preparation and cleaning
- Controlled configuration testing
- Accuracy-based evaluation
- Method and parameter documentation
- Reproducible technical reporting
04 / EDUCATION & ACHIEVEMENTS
A technical foundation built through evidence.
AWARDED SEPTEMBER 2026
BSc Computer Science
University of Southampton · 2:1
Built a practical foundation across software engineering, machine learning, data management, distributed systems and product delivery. The degree culminated in an independently designed and evaluated computer-vision dissertation in Python.
First Class Foundation Year
Completed the University of Southampton’s Engineering Foundation Year with First Class performance in June 2022.
Machine learning
Built and evaluated classification models in Python with scikit-learn across multiple configurations, covering preparation, feature selection, training and accuracy analysis.
Data management
Designed relational schemas and wrote SQL across multi-table datasets, including joins and aggregate queries checked against source records.
AI-assisted workflows
Have used Claude and ChatGPT as daily working tools since 2022 for coding, research and document workflows, checking output against sources before relying on it.
NEXT
If the problem is complex,
I’m ready to move it forward.
I’m looking for product, project and technology opportunities where technical understanding, decisive delivery and human judgement matter equally.
praisekapota@outlook.com