Sulochana kamshettey

Experienced Data Scientist and Advanced Analytics Specialist with 3+ years of experience at cyient delivering intelligent, end-to-end solutions across diverse sectors including Reliance Industries, e-commerce, aerospace, rail, transportation, NDG, and communications. Proven success in both long-term (6+ months) and short-term (

A key member of the Global Automation Team at Cyient headquarters, serving as a critical contributor to the Hyper automation unit, which delivered large-scale projects valued at $100K+ each, generating significant time, cost, and effort savings across the organization. Known for architecting multi-project solutions consolidated under a single framework—streamlining delivery across multiple clients and domains.

Demonstrated leadership in managing cross-functional teams of 15+ professionals, and driving successful project lifecycles from estimation (AML, TFS, BPD, VSM) to deployment. Delivered three impactful GenAI projects across aerospace, rail, and communications—including a video-to-text validation pipeline using OmniGPT-4 and Azure OpenAI that reduced 14+ hours of manual validation to under 1 hour for 20+ rail test cases.

Also collaborated with R&D teams on non-conformance text analytics in chemical product development, extracting actionable insights using NLP. Strong in strategic planning, stakeholder communication, and fostering cross-domain innovation using Generative AI and scalable accelerators

• Worked as a Data Scientist managing and analyzing large-scale datasets exceeding 4GB in volume, which involved complex data cleaning, transformation, and preprocessing pipelines to ensure high data quality for downstream analytics and modeling.
• I was responsible for developing robust monitoring frameworks to track model performance over time and detect data or concept drifts in production environments. Working with such high-volume data posed numerous engineering and analytical challenges, pushing me to continuously learn and adopt scalable solutions using distributed processing techniques and efficient data handling practices.
• This experience significantly enhanced my capabilities in big data ecosystems and taught me the value of efficient resource utilization, parallel processing, and automation. Each day brought new learnings—from optimizing data pipelines to resolving performance bottlenecks—making it a transformative phase in my career.