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Michelle Gomes
Product Lead | KYC Data products | Data assets, Advanced Analytics/AI, data wrapping
Michelle Gomes is a strategic thinker and experienced agile product manager and innovation professional currently working at ING.1 Her role at ING is Product Lead for KYC Data products, where she likely applies her expertise in product management and innovation to develop and improve Know Your Customer (KYC) data solutions for the bank.
Professional Background
Michelle has a strong educational background, having completed studies at Harvard Business School Online.1 This education, combined with her experience, has likely contributed to her current position as a product lead at a major financial institution like ING.
Skills and Expertise
As an agile product manager, Michelle is likely skilled in:
- Strategic thinking
- Product development
- Innovation management
- Agile methodologies
- Financial technology (FinTech)
- KYC processes and data management
Her role suggests she has a deep understanding of regulatory requirements in the banking sector, particularly those related to customer due diligence and anti-money laundering (AML) procedures.
LinkedIn Presence
Michelle Gomes' LinkedIn profile can be found under the username "michellegomes".1 Her profile likely provides more detailed information about her professional experience, skills, and achievements in the field of product management and financial technology.
Highlights
Thank you Ajit Jaokar , another point to add here is the responsibility to also communicate the boundaries and purpose of the model to business stakeholders.
Just read a book by David Spiegelhalter on 'The Art of Statistics: Learning from Data'
Statistics is difficult and probability is a complicated topic. However, David managed to summarise key issues when dealing with statistics Some highlights that I recognise in the book ▪︎ Asking 'why am I doing this' rather than jumping into analysis. ▪︎ Data quality is key. ▪︎ Keep communication simple and basic as possible. ▪︎ Make your analysis transparent and reproducible by providing details, disclaimers, assumptions used, approach taken, steps to reproduce , sample size, explain statistical significance in context. ☆ And the biggest lesson of all - be humble and open to learning.
Follow me on goodreads
https://lnkd.in/e82mVqgP
