Stakeholder & Team Management in Data Science: Principles andPractical Tips
Hrishikesh Vidyadhar Ganu, Mohak Sukhwani
hrishikesh.ganu@myntra.com,mohak.sukhwani@myntra.com
Myntra Designs Private Limited
Bangalore, Karnataka, India

TALK SUMMARY
Myntra Data Science (MDS) is one of the largest Machine learning
teams in India. Myntra has been an early adopter of ML solutions
and hence our work is deeply embedded across (1) In-app recommendations at all stages of the customer journey (2) Inbound supply
chain: sourcing of products, quality checks etc. (3) Outbound supply
chain-inventory allocation, route optimization, transit time forecasting etc. Because we are an applied ML team we work very
closely with stakeholders like Product and Business teams to deliver on the Annual Operating Plan (AOP) that’s broken down intoquarterly initiatives. Therefore managers in MDS not only needpeople manager skills to enable their direct reports to succeed but
also need skills required to collaborate effectively with Engineering,
Product and Business.
In the talk we propose to discuss (a) General principles of what
it takes to be a good ML people manager (b) Principles of how toeffectively collaborate with Business and Product stakeholders. (c)Real-life challenging scenarios we faced in our careers and how wehad to leverage these general principles to tackle those situations.
Here is s sample list of issues we plan to cover in our talk:(1) How to prioritise initiatives?
While Tech innovation and publications are very important
to Data Scientists innovation, per se, is of no value to Productor Business stakeholders unless it leads to an impact onCustomer Experience or business metrics like GMV. How toprioritise so that scientists’ motivation is kept high while
meeting business goals is a challenge.
(2) “Playing well” with other stakeholders
DS can get into conflict with other teams because DS is ascientific discipline while business runs on quick approximations. Eg: DS managers have to deal with unique cases where
due to business constraints randomized trials (AB tests) are
not possible. Frameworks like post-pre double difference
quite commonly used in business are not accurate. This cancreate a logjam unless DS is able to come up with a good
approximate measurement that satisfies all stakeholders.
(3) Appraisals: A balancing act
Data Scientists & Managers are expected to 1)deliver business impact 2)have deep technical contributions throughpapers 3)work well with other stakeholders and be team players. Managers have to coach employees on how to achieve abalance across these dimensions.
(4) Soft-skills training
Scientists are hired 1 from academic institutions largely

based on the strength of their technical contributions likepublications, ML techniques etc. However when they startworking in companies they also have to be taught how tonavigate inter-team conflicts and other such soft-skills to be
successful in their careers.
(5) Managing & planning timelines
Unlike a Software Development Engineer (SDE) who’s mostly
implementing business logic, an ML scientist can’t say by
how much the output metric will change before starting themodeling work. Due to the inherently iterative nature of theML workflow, it’s also tricky to provide accurate estimatesof the completion time for model development. In spite ofthese challenges we discuss how it’s possible to successfully
partner with Engineering & Program Management teams
that work in sprints and expect concrete outputs at a regularcadence.
We hope that through anecdotes and stories we will be able
to provide practical insights to DS managers while providing aframework of principles that could be leveraged when they facesimilar situations in their career.

SHORT BIO OF PRIMARY SPEAKER
Hrishikesh V. Ganu leads the Data Science team at Myntra. He has
17 years’ work experience in the areas of ML, Operations Researchand Statistics. Prior to Myntra, he has played managerial roles inML/DS teams at Intuit, Amazon among other companies. He has
a Master’s from Indian Institute of Science (IISc) and a full time
MBA from Indian Institute of Management (IIM), Kozhikode. He’s
several publications in ML conferences like SIGIR, NAACL, Informs
Conference on Business Analytics etc. He likes to learn as well ascoach people on the intricacies of building successful DS teams.

ABOUT THE COMPANY
Myntra 2 is one of India’s leading destinations for fashion, beautyand lifestyle and a pioneer in m-commerce play. An integral part ofthe Flipkart Group, Myntra brings together technology and fashion
to create the best experience in the fashion and lifestyle space inIndia. The company has partnered with over 7000+ leading fashionand lifestyle brands such as H&M, Levis, U.S. Polo Assn., Tommy Hilfiger, Louis Philippe, Jack & Jones, MANGO, Forever 21, Urbanic,
Marks & Spencer, W, Biba, Nike, Puma, Crocs, M.A.C, and Fossil
and many more, to offer a wide range in latest branded fashionand lifestyle wear. Myntra services over 27,000 pin codes across thecountry.

1 Data Science Hiring Process At Myntra: https://analyticsindiamag.com/data-science-

hiring-process-at-myntra/

2 https://www.myntra.com/