.
OTSUKA MIREVO Digital Health.
.
Key Tools and Frameworks: Jira, Confluence, AWS (S3, Lambda), Jenkins, Talend, Apache Spark.
Methodologies: Agile (Scrum), DevOps, SAFe, Clinical Compliance (HIPAA).
Domain: Healthcare, AI/ML, Clinical Informatics, and EHR Systems. Fin Nova NPBS Innovation (Nordea Personal Banking Service Innovation).
.
- Facilitated Agile delivery for an AI-driven platform supporting predictive diagnostics and real-time clinical recommendations across hospitals and care providers.
- Coordinated two Scrum teams building machine learning pipelines and user interfaces for doctors, radiologists, and lab technicians, ensuring sprint flow and clinical relevance of deliverables.
- Partnered with data engineers and healthcare SMEs to prioritize backlog features related to patient risk scoring, early diagnosis, and symptom prediction.
- Orchestrated collaboration with QA and compliance teams to conduct HIPAA-aligned testing and documentation, reducing rework and audit risks.
- Integrated delivery workflows with AWS-based data lakes and orchestration tools (e.g., Talend), enabling seamless EHR ingestion, and standardized patient data processing.
- Enabled DevOps maturity by guiding the setup of CI/CD automation using Jenkins, resulting in faster deployment of AI models, with rollback assurance.
- Drove continuous improvement by running retrospectives focused on clinical safety and data quality KPIs, leading to a 30% reduction in defect density over five releases.
.
Fin Nova NPBS Innovation (Nordea Personal Banking Service Innovation)
.
Key Tools & Frameworks: Jira, Confluence, Jenkins, AWS, Docker, Python (for AI team integration), SAFe.
Methodologies: Agile (Scrum), DevOps, Scaled Agile, AI/ML Delivery.
Domain: Banking, AI & Automation, Regulatory Compliance, Cloud-Native Services.
.
- Led Agile execution for an AI-enabled banking transformation project focused on intelligent document processing, chatbot integration, and personalized financial insights.
- Acted as a Scrum Master for two cross-functional pods—data scientists, backend engineers, and testers—ensuring iterative delivery of AI microservices within 2-week sprint cycles.
- Collaborated with Product Owners and SMEs to break down epics related to fraud detection and customer onboarding into actionable user stories, enhancing backlog readiness by 30%.
- Ensured AI model deployment cycles were seamlessly integrated into CI/CD using Jenkins and Dockerized containers on AWS, reducing go-to-market time for new ML models by 35%.
- Navigated regulatory compliance (KYC/AML) during sprint reviews, ensuring model behavior aligned with data privacy and audit readiness across sprints.
- Established Sprint KPI dashboards to track model accuracy trends, release cycles, and impediments, which led to improved transparency and model refinement feedback loops.
- Enabled scaled coordination across AI squads by facilitating quarterly PI Planning events under SAFe, ensuring inter-team alignment on roadmap goals, and delivery cadence.