Madhavi Kaivalya Kandalam | Understanding AI trends and risks

https://is1-ssl.mzstatic.com/image/thumb/Podcasts114/v4/1f/49/d3/1f49d31f-d45e-cff4-75da-c7aaf9f3e3d8/mza_15094187412564979323.jpg/600x600bb.jpg

Data Science Sagas

Women in Data Science Mumbai Team

6 episodes

3 days ago

A podcast show brought to you by Women in Data Science (WiDS) Mumbai team featuring local Data Scientists sharing their work, advice, and lessons learned along the way. Hear about how data science is being applied and having an impact across a wide range of domains.

Technology

RSS

All content for Data Science Sagas is the property of Women in Data Science Mumbai Team and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.

Technology

https://d3t3ozftmdmh3i.cloudfront.net/production/podcast_uploaded_episode/6628157/6628157-1643717190331-af7587033daa6.jpg

Madhavi Kaivalya Kandalam | Understanding AI trends and risks

Data Science Sagas

16 minutes 5 seconds

3 years ago

Madhavi Kaivalya Kandalam | Understanding AI trends and risks

About the speaker:
Joining us today is Madhavi Kaivalya Kandalam. She has a B.Tech in Biotechnology from NIT Warangal.
Starting with Data Science consulting for Fortune 500 companies at Mu Sigma, she moved onto an in-house Data Science function at the largest loyalty service provider in India and progressed to
a Corporate Leadership position as the Chief Data Scientist. Working at two start-ups in their growth phase has been a
tremendous learning opportunity for her.
Her recent projects include:
- Building a FinTechMarketing product targeting with ML algorithms at backend for automatic target group selection
- Creating POC for AI based recommendation engine to improve customer engagement on banking transactions
She is currently pursuing her MBA from the London Business School

About the conversation:
In this episode, we talk about:
1. Her journey
2. Latest trends in business models in deep tech using AI:
a) Successful deep tech ventures are bringing together multiple talents (including scientists, engineers, and entrepreneurs) to solve a problem.
Often they develop brand-new technologies because no existing technology fully solves the problem at hand.
b) Infrastructure to store and manage data still remains a bottleneck for the efficiency of AI solutions but is consistently being worked upon. For instance, Kafka is making the streaming of real-time data seamless
c) AI bias: Over the past few years, society has started to wrestle about human biases that can make their way into artificial intelligence systems — with harmful results.
At a time when many companies are looking to deploy AI systems across their operations, being acutely aware of those risks and working to reduce them is an urgent priority.
3. Advice for breaking into Data Science:
a) Talk to people already in the field and don't hesitate to get your hands dirty in the code. Coding is an essential stepping stone for this field irrespective of your role.
b) Start small and figure out your path one step at a time
c) Don't hesitate to learn every day
4. Is an MBA necessary?
MBA is a personal decision and is circumstantial. You can always transition to a leadership role without an MBA but if you want to get one, be clear with the expectations from the degree.
5. Her plans post MBA: She plans to launch a startup in the EdTech space.