On MathCo’s 7th Anniversary, Cofounders Aditya Kumbakonam, Anuj Krishna, and Sayandeb Banerjee candidly answer 7 questions about what’s on their mind right now, and what they expect in the future for MathCo.
In the episode, the three Cofounders reintroduce themselves, share something that most people don’t know about them, and talk about what they’d be doing if they weren’t doing this for a living.
They answer 2 unique questions and one common question:
Listen to the whole thing so you don’t miss the ✨ surprise ✨ at the end.
You can find the transcript of the episode here: bit.ly/3PzGryN
It’s Women’s History Month and earlier this month, the AI Xecutive Council (an initiative of TheMathCompany) organized Conversations for Change, a roundtable where leaders in the data analytics and AI space came together to discuss representation of women in tech and data. Listen to them address important questions such as: What do we need to do to close the gender gap in tech & data analytics? How can companies increase representation of women in the space? What kind of policies can help? How did these women leaders overcome challenges to get here? How can we achieve equity?
Topic: Paving Diverse & Resilient Pathways to Technology Careers
Moderated by Sadie St. Lawrence, Founder & CEO, Women in Data
Speakers:
Telina Reil - Director, Advanced Analytics, Abbott
Claus Rose - Vice President, EHS Renewable, GE
Ling Zhang - VP of Data Science, Cadent
Gustavo Mendonca - Director, Global Head of RGM Digital Solutions, The Kraft Heinz Company
Praveena R - Senior Director, Data Engineering & Analytics, Dollar General
Neethu Vincent – Principal, TheMathCompany
Anjali Iyer – Associate Principal, TheMathCompany
Sahana Sreeja – Associate Principal, Solutioning, TheMathCompany
Nicole Crosby – Associate Principal, Customer Success, TheMathCompany
Liz Taylor – Principal, Human Resources, TheMathCompany
Maria Cecilia Hutcheson – Head of Region, Strategic Accounts, TheMathCompany
Learn more about TheMathCompany here. Learn more about the AI Xecutive Council here.
As 2020 comes to a close and 2021 begins amidst great expectations, catch our Co-Founders, Sayandeb Banerjee, Aditya Kumbakonam, and Anuj Krishna, engage in insightful conversation on life as we have never known it before, the challenges of the past year, and the journey ahead for TheMathCompany, in this podcast hosted by Pranav Sharma.
In this podcast session, Amit Kurhekar, Director Data Science Solutions, Yodlee, an Applied Data Science Leader with close to two decades of experience in the field of analytics, delves into the world of industry 4.0, with Srinidhi Rao, Senior Partner, TheMathCompany. Amit shares his expert insights on the tools, technologies, skill sets and operational structure required to adopt industry 4.0, explains how progress can be quantified or measured based on an organization’s adoption journey and shares his insights on the possibility of leveraging industry 4.0 to cushion the impact of pandemic disruption.
What is Industry 4.0?
What are the tools, technologies and skillsets required to adopt industry 4.0?
What should be the operational structure/hierarchy of an Industry 4.0 team?
Are there examples or use case where industry 4.0 has offered companies a sound ROI?
What’s the distinction between pureplay automation and industry 4.0?
How do you know you quantify/measure progress in your industry 4.0 adoption journey?
Is industry 4.0 a feasible investment for small and mid-sized companies?
In retrospect, could Industry 4.0 could have cushioned the impact of COVID19?
In this podcast, our host Srinidhi Shama Rao, Senior Partner, TheMathCompany, interviews Ishan Basu, Partner, TheMathCompany, who has a wealth of experience in the field of analytics, across Retail, CPG & Insurance industries. Catch this podcast for pertinent insights on how behavioral analytics is making huge waves and shaping decisions in industrial and political landscapes.
Tune in!
Timeline:
0.00s – Introduction
1.44s – What’s the next big thing in analytics that can add immense value to organizations
2.17s – The large gold mine of customer data & insights that brands can leverage
5:34s – What is Behavioural Analytics?
7.07s – How a Pizza Delivery Business Tailored Services by Tuning into Customer Cues.
9.12s – Attitudinal Segmentation of Customers
10.50s – How & Where Do You Tap into Keen Customer Data & Insights?
15.12s – How Would This Play Out in Automobile, Retail, CPG or BFSI Industries?
24.13s – Behavioral Analytics in Societal & Political Landscapes
26.24s – How Can Data Scientists Start Leveraging Behavioral Analytics?
30.27s – Conclusion
In this podcast Srivatsa Kanchibotla, one of the Top 10 Data Scientists in India 2019, discusses how the COVID19 pandemic has uprooted lives, business operations and economies world over. He takes a keen look at data and research studies, to offer his guesstimate on what we can expect in the near future.
Tune in!
Podcast Timeline:
0:00s – Intro
0:44s – COVID-19 & the ripple effects it has had across the globe
5:45s – Would the ongoing recession be sharper than the global financial crisis in 2008?
6.35s – (Audience interaction) Production & Distribution of food in India and the US
7:30s – History of pandemic responses
7:50s – How do you know someone has COVID-19?
10:48s – A public health or medical issue?
11:28s– How will COVID19 impact private consumption and industries?
12.25s – (Audience Interaction) Will the sharp decline be followed by sharp increase in recovery, thereby minimize long term ramifications?
14:51s – What about vaccines?
15.24s – How have different governments responded to the vaccine?
18.36s – How will organizations reinvent themselves to adapt to the changes in the long run?
20.15s – (Audience Interaction) Is COVID-19 a black swan event?
21.58s – Sign-off
In this episode of Express Q & A sessions, Sandeep Kalyanasundaram, Partner at TheMathCompany, chats with Srinidhi Shama Rao, Senior Partner at TheMathCompany and with over 16 years of experience in the analytics industry.
In this session, Srinidhi and Sandeep discuss the aspects that enable businesses to leverage HR analytics and the challenges to take into account when undergoing digital transformation.
With the advent of AI and automation, the fundamental way of working is shifting in the industry today. Anything that can be digitized or automated to help human effort, is being done. This transformation is redefining traditional working methods and changing the skillset of who and how we hire. In this regard, Human Resource (HR) functions are more important than ever before.
Tune in to this podcast to understand ways through which HR analytics can be leveraged to thrive in the face of an industrial transformation.
Content Timeline:
0:5s- 0:25s - Sneak peek
0:28s - Introduction
0:37 s - Why is there a buzz with regard to using AI and automation in HR now?
1:40 s - How do you think HR organisations should respond to the constant changing nature of the industry?
2:15 s - As with any analytical journey, HR organizations will be faced with challenges like getting the right stakeholders, procuring the needed data and driving consumption. What are some challenges that are unique to HR?
4:00 s- HR analytics has transformed in the last decade. Where do you think organizations are today, when it comes to maturity of HR analytics?
5:18s - What kind of data should organizations collect in order to answer questions around HR analytics?
6:23s- How is TheMathCompany placed in helping organizations traverse HR analytics journeys?
7:10s- What do you think HR teams should do to thrive in the face of disruption and help organizations go through analytical transformation?
Pranav Sharma, Partner, TheMathCompany has over 10 years of experience in the analytics eco system. Pranav has extensively in delivery and client engagement roles, and worked with Fortune 500 clients, across multiple business functions in the CPG, Retail and telecom industries.
In the session, Pranav delves into the principles of design thinking, how they can be inculcated in our day to day life, and analyzes how these methods by which we design everyday items and activities, also frame the thinking behind complex AI structures. Tune in to this session to get vital insights into this data science function - there are some interesting activities and Q&A segments that you wouldn't want to miss!
Content Timeline:
35s- Introduction to the session
3:02s - What is design thinking and why is it needed ?
5.58s - The principles of design thinking
11 mins - Interactive activity: How can you design a better atm for a bank?
13.44s- Introduction to design thinking in analytics
14.30s - Lifecycle of a typical data science project
22.15s - Artefacts and rituals that help build effective design paradigms
Interactive Q&A session:
1) 28.42s- How do we know which design approach works best?
2) 29.15s - How many artefacts would one generally require?
3) 30 mins - What are the challenges faced in executing the design approach discussed?
4) 23:45s - How can you get a team comfortable with a current way of problem-solving to move a design thinking-based approach?
6. 24:41s - Is there any platform to access all the artefacts that can be used?
Tune in to this insightful Express Q&A session where Srinidhi Shama Rao, Senior Partner at TheMathCompany, chats with Srivatsa Kanchibotla, Senior Partner at TheMathCompany and recognized as one among the Top 10 Data Scientists in India 2019.Srivatsa has over 10 years of experience in the Data Science industry, and his tenure has seen him work closely with more than 15+ Fortune 500 companies, across BFSI, Technology, Hospitality, Retail and E-Commerce industries, enabling multi-million dollar impact for clients, and also building and managing large teams of Data Engineers and Data Scientists. Srivatsa is known for his unique innovations and unparalleled accomplishments in the industry.In this podcast session, Srinidhi Shama Rao and Srivatsa Kanchibotla discuss advances in ML Engineering, the shift towards a productized landscape, evolving industry expectations from data scientists, and the convergence of ML engineering and Data science disciplines.Get the lowdown on all this and more, in this podcast session.
Podcast Timeline:
0s to 33s - sneak-peek
34s to 47s - Intro
48s - Q1: What is this role called ML Engineering and why is there so much buzz around it?
2:00s - Q2: How easy or difficult is it to make engineering a product or a platform that can cater to most businesses, if not all? What steps are companies taking in this direction?
3:28s - Q3: Is the paradigm of the approach to modeling changing as platform play is coming into the picture? Is it moving on from traditional ways in which we would approach statistical models int the past to more ML-based ways of doing things? If yes, what are they?
5:08s - Q4: As platform play is dominating the world and the world is getting more streamlined and standardized, how is the approach to modeling changing from the statistical ways in which we approached things in the past?
6.57s - Q5: In this new world order, what skills should data scientists be equipped with? How is their world-changing with respect to the new skills that are required to be successful and talk to other peripherals?
8.25s - Q6: As the role of the platform and the data scientist skillset changes, what are the biggest barriers or challenges today for organizations to deploy models, productionalize, and extract value out of them? What is the biggest barrier to value?
10.55s - Q7: Do you see ML Engineering and Data Science roles converging at some point?
11.56s - Q8: As the world gets more productized and in a platform-based paradigm, what is the role of scale? What are its challenges as compared to the scenario a decade ago? Have these challenges changed?
13.25s - Q9: For a minute, I want you to take off your ML Engineer hat and look at this from an outsider perspective. As you look forward 3 to 5 years in the future, do you things becoming more product-dominated or services-dominated?
15.10s - Sign-off
The COVID-19 situation has brought about major disruptions in business operations and industry dynamics across the globe. While it is impossible to predict what the new normal will be when we emerge from the dire situation, for now, we can make educated estimates in some areas, that puts us in a better position to face the post-COVID era. Catch Sayandeb Banerjee, Co-founder & CEO of TheMathCompany, Member of Forbes Technology Council, share his expert views on the subject in this podcast on Life, Business, Politics and Role of AI in a Post-COVID world.
Key insights:
Catch Srinidhi Shama Rao, Senior Partner at TheMathCompany, in conversation with Anindo Chakraborty, Director, North America Zone Sales Analytics at Growth Analytics Center, Anheuser Busch InBev. Anindo has had an illustrious career of 19 years in the analytics industry, starting with GE, moving to Target, where he worked on many interesting, high-impact projects, and then transitioning to his current role as Director at Anheuser Busch InBev. In this podcast session, Srinidhi Shama Rao and Anindo Chakraborty address a range of topics pertinent to the changing landscape of the data science industry. They also spill the beans on must-have skillsets for aspiring data scientists and analytics managers in order to pursue a successful career in this field. If you are an aspiring analytics leader, looking forward to staying on the top of your game - listen with a keen ear, because Anindo Chakraborty has some valuable tips to share.
Podcast timeline:
0.00 to 0:37s – Sneak-peek
0:37s to 01:34s - Introduction
1:34s - Q1: What do you think differentiates successful analytics teams from others?
6:01s - Q2: What kind of skills do you look for when you hire people for these teams? What are the skills that are needed during the time of hiring, which ones can be learned after?
9:33s - Q3: What do you think is the best way to inculcate domain knowledge in these teams?
12:48s - Q4: How do you look for the right team structure?
15: 06s - Q5: You have been one of the most successful analytics leaders in the industry today, across organizations. How do you perceive your role as a leader - what traits set you apart from your peers?
18:42s - Q6: Today, Data Science and analytics as a market is exploding and lots of great talent are looking forward to working in the field. What is your advice to young, aspiring data scientists?
21:52s - Q7: Many good data scientists are grooming themselves to become analytics managers and senior managers. What kind of attitude and skillset should one develop to become successful analytics leaders in the future?
