speaker detail

Mathangi Sri

Chief Data Officer

company logo

Mathangi Sri has a proven track record in building world-class data science solutions and products. She has overall 20 patent grants in the area of intuitive customer experience and user profiles. She authored and published a book with Apress, Springer - "Practical Natural Language Processing with Python." She recently published her second book with BPB Publications - 'Capitalizing Data Science.' She is currently the Chief Data Officer at Yubi. She has previously built data science teams across large organizations like Citibank, HSBC, and GE, and tech startups like 247.ai, PhonePe, and Gojek. She has brought about cultural change & shift in mindsets for adopting data-driven decisions across different startups. She actively contributes to the Data Science community - through lectures, talks, blogs, and advisory roles.

She is a guest faculty member at many premium academic institutes nationwide, such as IIIT Sri City, IIM Kashipur, NIT Trichy, etc. She is recognized as one of "The Phenomenal SHE" by Indian National Bar Association in 2019,10 most influential leaders in BFSI by Analytics India Magazine 2024, Trailblazer Visionary recognized for Leadership in Artificial Intelligence by ET Edge 2024, CDO/CTO/CIO of the year - Bharat Fintech Summit - Fintech & Digital Excellence Awards 2024, 50 most powerful influencers in AI 2022 by Engatica, top 50 Influential AI leaders in India by analytics India magazine in 2021, top AI leaders in India 2021 by 3AI association.

We explain step by step how we leverage Gen AI to generate highly intelligent and well-interpreted financial analysis and commentary for decision-making. The process consists of data standardization, normalization, computing ratios, the RAG layer, prompt engineering, and generation. We also will explain the importance of domain expertise and HIL(Human in the loop) in large-scale Gen AI solutions.

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
Book tickets View all speakers

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details