Product Manager Data Center

AADITYA CHAUDHARI

San Francisco, CA (Open to relocation) *******************@*****.*** 669-***-**** LinkedIn Summary

• Product Manager with 5+ years of technical experience in Fintech, Healthcare, Enterprise solutions, and E-Commerce, as well as proven expertise in managing end-to-end product lifecycles across Data Center technologies, Networking, B2B SaaS, and Developer Tools.

• Partnering with cross-functional teams (engineering, UX/UI design, and marketing) to deliver real-time software applications and build 0-1 products.

• Robust in defining technical product requirements, directing Agile development processes (Scrum, Kanban), and improving system efficiency through microservices architecture, AWS cloud infrastructure, CI/CD pipelines, and Generative AI.

• Experienced in identifying customer needs using data-driven insights, A/B testing and compliance standards (GDPR, COPPA), executing successful product launches, and enhancing system reliability while aligning technical strategies with overall business objectives for measurable product success.

• Strong technical background in data Analysis, microservices architecture, cloud infrastructure, and API integrations. Skills

• Product Management: Roadmap planning, Product lifecycle management, Data Analysis, Stakeholder management, User research, Go-to-market strategy, and Customer journey mapping.

• Agile Methodologies: Sprint planning, Scrum, Kanban, Backlog prioritization

• Technical Skills: Python, Java, R, JavaScript, SQL, RESTful APIs, Microservices, CI/CD pipelines Gen AI (GPT, DALL·E).

• Tools: Jira, Trello, Excel (Advanced), Tableau, Google Analytics, SharePoint, Figma, Miro, Git, Power BI, AWS, Google Cloud

• User Experience (UX): Wireframing, A/B testing, Prototyping, User journey mapping

• ML: Deep Learning, Scikit Learn, TensorFlow, Keras, PyTorch Work Experience

Outlier San Francisco, CA

AI Product Manager Oct 2024 - Present

• Evaluated AI-generated responses across multiple use cases, refining machine learning model accuracy by 20% through continuous feedback cycles and performance monitoring.

• Designed real-time AI tracking dashboards using SQL, Tableau, and Python to measure model drift, customer engagement trends, and product performance metrics.

• Developed structured methodologies to analyze precision, recall, and F1 scores for NLP-powered chatbots, improving response accuracy and relevance.

• Coordinated closely with engineering, UX, and business stakeholders to streamline the deployment of AI-driven product enhancements, ensuring minimal disruption to customer workflows.

• Established a data-driven prioritization framework, using customer insights and user analytics to rank feature requests and optimize product roadmap.

• Led cross-functional knowledge-sharing sessions to bridge the gap between technical teams and business stakeholders, increasing organizational understanding of AI performance.

• Designed A/B testing experiments to validate the impact of AI-driven recommendations, ensuring improvements were aligned with user needs and business objectives.

• Worked with legal and compliance teams to implement responsible AI practices, ensuring all models adhered to GDPR, CCPA, SOC2, and enterprise risk management policies.

ServiceMob Irvine, CA

Product Manager Sep 2024 - Present

• Developed and implemented AI-driven workflow automation tools, leading to a 30% reduction in customer service ticket resolution times and improving first-contact resolution rates.

• Built a real-time analytics pipeline to process millions of customer interactions, extracting actionable insights that shaped product features and efficiency.

• Led weekly sprint planning meetings, ensuring that engineering teams delivered high-impact features on time while aligning with business priorities.

• Translated complex technical requirements into well-defined user stories and acceptance criteria, enabling seamless execution in Agile cycles.

• Conducted in-depth user research and usability testing, leveraging customer feedback to refine product features and increase adoption rates.

• Implemented predictive analytics models that proactively identified customer pain points, leading to a 15% increase in customer satisfaction scores.

• Worked closely with customer success and sales teams to develop data-backed onboarding strategies, improving product retention and reducing churn.

• Analyzed competitive market trends and conducted benchmarking studies to identify differentiation opportunities, ensuring the product remained ahead of industry standards.

California State University, Fullerton Fullerton, CA Python Development (Teaching Associate) Jan 2023 - Aug 2024

• Designed a data-centric curriculum incorporating real-world machine learning and cloud computing applications, improving student engagement and skill acquisition.

• Developed hands-on coding exercises and structured learning pathways, enabling students to gain practical experience with Python, SQL, and data visualization tools.

• Implemented an adaptive learning model, using analytics to adjust teaching methods based on student performance and comprehension metrics.

• Established a structured peer mentorship program, encouraging knowledge-sharing and collaborative problem-solving in technical coursework.

• Created interactive learning assessments, leveraging gamification techniques to boost student participation and retention.

• Introduced A/B testing methodologies to compare teaching techniques, refining instructional approaches based on data-driven insights. Research Manager July 2023 – Aug 2024

• Developed an AI-driven security framework integrating Natural Language Processing (NLP) and deep learning for automated Hardware Trojan detection in Field-Programmable Gate Arrays (FPGAs), improving threat identification accuracy by 35%.

• Designed a real-time anomaly detection model using Long Short-Term Memory (LSTM) networks, leveraging Python, TensorFlow, and PyTorch to analyze hardware description language (HDL) files, reducing manual security reviews by 50%.

• Applied NLP-based sequence modeling with LSTM architectures to extract and classify malicious signatures in Verilog and VHDL designs, enhancing the accuracy of pattern recognition and threat classification.

• Built a cloud-integrated security system using AWS and edge computing, enabling scalable real-time threat analysis for enterprise and defense applications.

• Collaborated with hardware engineers, AI researchers, and cybersecurity teams to optimize hardware verification processes, improving detection efficiency and security compliance.

• Ensured alignment with NIST and ISO 27001 security standards, positioning the research for adoption by semiconductor manufacturers and government agencies.

• Published findings in IEEE ISVLSI 2024, contributing to cutting-edge AI-driven cybersecurity protocols for hardware security, semiconductor supply chains, and mission-critical applications.

Varsha Kokila Pvt. Ltd. Vadodara, India

Technical Product Manager Jun 2019 - Jun 2022

• Collaborated with engineering, design, and business teams to define the end-to-end product lifecycle for B2B SaaS trading solutions, successfully delivering a scalable FX Payments platform that improved transaction efficiency by 40% through optimized cross-border payments and real-time settlement processing.

• Defined API architecture and integrated third-party payment providers, ensuring seamless multi-currency transactions and compliance with ISO 20022 payment messaging standards, which enhanced global remittance processing and expanded the platform’s reach.

• Managed the product roadmap and backlog prioritization using Jira, implementing Agile methodologies (Scrum, Kanban) to coordinate efficiently with development teams, reduce release cycle times, and improve sprint velocity by 25%.

• Partnered with engineering teams to implement robust API integrations, scale microservices architecture, and ensure regulatory compliance with GDPR, COPPA, PCI-DSS, and SOC 2, improving data security, payment reliability, and transaction transparency by 30%.

• Designed and executed competitive analysis frameworks, analyzing market trends, customer pain points, and evolving fintech regulations to refine the product strategy and introduce high-impact data center technologies that improved system uptime and scalability.

• Conducted A/B testing on UI components and transaction workflows using Tableau and Power BI, optimizing conversion funnels and achieving a 35% increase in user engagement and satisfaction by refining onboarding flows, authentication mechanisms, and payment reconciliation features.

• Led go-to-market strategy (GTM) for new financial products, working with marketing and sales teams to craft value propositions, pricing models, and adoption strategies, ensuring product positioning aligned with industry demand and regulatory changes.

• Worked closely with compliance teams to implement AML (Anti-Money Laundering) and KYC (Know Your Customer) frameworks, ensuring adherence to FinCEN, FATF, and local regulatory requirements, reducing fraud risks, and improving trust among institutional clients. Purva Enterprise Vadodara, India

Software Engineer Jun 2018 - May 2019

• Developed and optimized scalable backend data pipelines and automated ETL workflows using Python, SQL, and Apache Spark, enabling seamless data ingestion and transformation for Electronic Health Records (EHR), insurance claims, and billing transactions.

• Designed and implemented RESTful APIs for healthcare data integration, ensuring secure and efficient communication between hospital databases and third-party services and improving data accessibility.

• Engineered a high-performance SQL-based reporting system, optimizing complex query execution and indexing strategies, reducing database response times by 50% for financial reporting and compliance tracking.

• Built and deployed real-time analytics dashboards using Tableau and Power BI, integrating backend APIs to provide actionable insights on patient demographics, reimbursement trends, and provider efficiency.

• Developed predictive analytics models in Python to detect fraudulent billing patterns, automate claim denial analysis, and support compliance with HIPAA, CMS, and ICD-10 regulations.

• Implemented A/B testing frameworks and policy impact assessments, leveraging statistical models to evaluate reimbursement rate changes, patient outcomes, and provider efficiency, ensuring compliance with Medicare and Medicaid billing regulations.

• Enhanced data security and interoperability, integrating FHIR and HL7 standards to enable seamless communication between payer-provider networks, improving healthcare data exchange efficiency.

• Defined software-driven data governance policies to ensure system integrity, security, and compliance, supporting audit readiness, operational efficiency, and predictive analytics in Revenue Cycle Management (RCM). Projects

Plant Disease Classification

• Developed and launched an AI-powered mobile application using CNN and TensorFlow, achieving 97% accuracy in detecting plant diseases from real- time images.

• Designed an intuitive Android app interface, incorporating user feedback from farmers to improve accessibility and enhance usability for non- technical users.

• Built an automated model retraining pipeline using AWS (S3, Lambda, SageMaker), ensuring continuous accuracy improvement based on real-time user-submitted data.

• Implemented cloud-based data processing to enable scalable image classification, reducing inference time by 20% while maintaining high precision.

• Defined go-to-market strategy and feature prioritization, aligning business goals with agriculture industry needs for broader adoption and impact.

• Integrated edge computing capabilities to process image classification locally on mobile devices, reducing latency and reliance on cloud-based inference for users in low-connectivity regions.

• Conducted A/B testing and model performance evaluation, refining AI algorithms and user experience based on real-world agricultural conditions, leading to a 15% increase in user engagement.

Movie Dataset Analysis using Hadoop

• Developed a large-scale data processing pipeline using Hadoop and Apache Spark, improving data accuracy by 50% and reducing processing time for movie trend analysis.

• Designed a cloud-based ETL workflow using AWS Glue, Redshift, and S3, optimizing real-time data ingestion and transformation for enhanced analytics.

• Built an interactive Power BI and Tableau dashboard, providing stakeholders with actionable insights into box office performance, audience demographics, and genre trends.

• Implemented predictive analytics models with NLP-driven sentiment analysis, identifying audience preferences based on movie reviews and social media sentiment.

• Ensured compliance with data privacy regulations (GDPR, CCPA) by establishing role-based access controls and audit logging mechanisms for secure data processing.

AI-driven Customer Support Automation

• Led the development of an AI-powered customer support chatbot leveraging NLP models, sentiment analysis, and automated response generation, reducing support ticket volume by 40%.

• Integrated the chatbot with CRM platforms (Salesforce), enabling real-time customer interaction tracking and improving issue resolution speed.

• Implemented a feedback-driven AI optimization loop, using user sentiment scoring and behavioral analytics to refine chatbot accuracy.

• Worked with UX/UI teams to design conversational flows and decision trees, improving chatbot adoption and customer engagement rates.

• Defined product metrics and KPIs using A/B testing and analytics dashboards, driving data-driven improvements in customer satisfaction scores

(CSAT) and first-call resolution rates.

E-Commerce Recommendation System for Personalized Shopping

• Developed a machine learning-based recommendation engine using collaborative filtering and deep learning models, increasing conversion rates by 25%.

• Implemented real-time product recommendation APIs, integrating with web and mobile platforms to enhance personalized shopping experiences.

• Conducted user behavior analysis using SQL, Python, and Tableau, identifying trends in customer purchase patterns and optimizing recommendation algorithms.

• Worked with engineering teams to deploy scalable microservices-based architecture, improving load handling and reducing latency for product recommendations.

• Defined and tracked key engagement metrics (CTR, bounce rates, average order value), refining recommendation strategies through continuous A/B testing.

Education

California State University, Fullerton Fullerton, CA Master of Science in Computer Science GPA – 3.7/4.0 Gujarat Technological University Vadodara, India

Bachelor of Engineering in Computer Science GPA – 8.72/10.0 Publications

• IEEE ISVLSI 2024: Natural Language Processing Meets Hardware Trojan Detection: Automating Security of FPGAs.

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