I shaped the AI insights platform roadmap used by 12,000+ users and lifted feature delivery accuracy by 20 percent by reducing “time to insight.”
I obtained steady delivery by writing clear user stories and acceptance criteria in Jira, which cut sprint rollover by 30 stories.
I improved AI model performance by reviewing LLM outputs in Python and tracking confidence scores in Power BI, raising accuracy by17 percent.
I raised stakeholder clarity by 15 percent by keeping documentation sharp in Confluence and running alignment sessions.
I increased on-time delivery by 25 percent through OKR-led prioritisation and reducing rework with A/B test insights.
I boosted delivery flow by analysing sprint data to surface blockers early, cutting delays by 20 stories and raising velocity by 22 points.