Nitin C

0-to-1 product development for a B2B clinical AI platform. Owned discovery, roadmap, build, and deployment. 1. Owned end-to-end product development across multiple surfaces (Admin, Core Workflow, Reporting, Review, Analytics), supporting the full user journey 2. Led product discovery through 20+ user interviews and on-site workflow shadowing, translating qualitative insights into structured requirements 3. Designed agentic clinical AI workflows - defining prompt requirements, confidence thresholds, validation gating, and structured output checks - reducing task rework by ~20% via UAT retry events. 4. Owned roadmap planning using data-backed trade-offs across technical feasibility, UX, model performance, and compliance - including prioritizing real-time AI assistance over post-processing 5. Embedded data-driven decision-making by tracking task completion time, adoption metrics, workflow drop-offs, and usability benchmarks 6. Led usability testing and UAT across 10+ customer environments, defining measurable success criteria and iterating based on observed usage patterns 7. Drove cross-functional execution across engineering, AI, QA, and BD in rapid sprint cycles - authoring PRDs and deployment playbooks that cut ramp-up time by ~40% and IT onboarding effort by ~40-50%. 8. Led GTM feasibility and OEM prioritization - analyzed market coverage, integration effort, and deployment risk to define a phased compatibility roadmap covering ~60-70% of the US target install base. Impact: - ~88% reduction in manual user interactions - ~50% faster task completion in usability studies - ~25% improvement in usability scores across releases - ~30% reduction in inter-operator variability - 10+ live enterprise deployments

Worked on applied analytics and early-stage AI use cases for industrial systems, focusing on turning raw sensor data into operational insights. What I worked on: 1. Analyzed time-series sensor data from industrial mud pump systems to identify performance degradation patterns and operational inefficiencies 2. Conducted root-cause analysis on anomalies in pump behavior, helping teams distinguish between normal variance and early failure signals 3. Built analytical scenarios to support predictive maintenance and reduce unplanned downtime in Oil & Gas operations 4. Collaborated with domain experts and engineering teams to translate analytical findings into actionable operational recommendations 5. Supported early problem framing for AI-driven optimization use cases, gaining hands-on exposure to how data products are scoped, validated, and operationalized Impact highlights: 1. Contributed to initiatives that improved asset uptime by ~5% 2. Helped reduce unplanned downtime by ~10% through data-driven diagnostics 3. Enabled ~7% improvement in operational productivity by informing maintenance and operating decisions