Data Scientist IV - Meesho

Job Title: Data Scientist IV
Job Location: Bangalore, Karnataka
Job Listing URL: https://jobs.lever.co/meesho/38849bdb-6277-46ab-8865-7d9d83d43ba7
Job Description: About the Team

Our Data Science team is the Avengers to Meesho’s S.H.I.E.L.D 🛡️. And why not? We are the ones who assemble during the toughest challenges and devise creative solutions, building intelligent systems for millions of our users looking at a thousand different categories of products. We’ve barely scratched the surface, and have amazing challenges in charting the future of commerce for Bharat.

Our typical day involves dealing with fraud detection, inventory optimisation, and platform vernacularisation.

As Lead Data Scientist, you will navigate uncharted territories with us, discovering new paths to creating solutions for our users.🔍 You will be at the forefront of interesting challenges and solve unique customer problems in an untapped market.

But wait – there’s more to us. Our team is huge on having a well-rounded personal and professional life. When we aren't nose-deep in data, you will most likely find us belting “Summer of 69” at the nearest Karaoke bar, or debating who the best Spider-Man is: Maguire, Garfield, or Holland? You tell us ☺️

About the Role

Love deep data? Does ‘innovative-thinking’ describe you? Then you may be our next Lead Data Scientist.

In this role you’ll be the Dumbledore to our team of wizards - our junior data scientists. You will be responsible for transforming scattered pieces of information into valuable data that can be used to achieve goals effectively. You will extract and mine critical bits of information and drive insightful discussions that result in app innovations.
What you will do
Own and deliver solutions across multiple charters by formulating well-scoped problem statements and driving them to execution with measurable impact
Mentor a team of data scientists (DS2s and DS3s), helping them with project planning, execution, and on-call issue resolution
Design and optimize key user journeys (e.g., Reseller Experience, Search, Fraud Systems) by identifying user intents and behavioral patterns from large-scale data
Collaborate with machine learning engineers and big data teams to build scalable ML pipelines and improve inference performance
Continuously track and improve model performance using state-of-the-art (SOTA) techniques and libraries
Lead experimental design for usability improvements and user growth, leveraging statistical rigor
Contribute to system-level thinking by enhancing internal tools, frameworks, and libraries to improve team efficiency and code quality
Partner with engineering to ensure data reliability, compliance with security/PII guidelines, and integration of models into production systems
Proactively explore new areas of opportunity through research, data mining, and academic collaboration, including publishing and attending top-tier conferences
Communicate findings, plans, and results clearly with DS, product, and tech stakeholders, and create technical documentation consumable by both DS and engineering teams
Conduct research collaborations with premier colleges and universities Attend conferences and publish research papers
What you will need
A Bachelor's degree in Computer Science, Data Science, or a related field; a Master’s is a plus
6–8 years of experience in data science with a strong track record of building and deploying ML solutions at scale
Deep understanding of core ML techniques — supervised, unsupervised, and semi-supervised learning — along with strong foundations in statistics and linear algebra
Exposure to deep learning concepts and architectures (e.g., CNNs, RNNs, Transformers) and their practical applications
Proficiency in Python and SQL, with experience in building data pipelines and analytical workflows
Hands-on experience with large-scale data processing using Apache Spark, Hadoop/Hive, or cloud platforms such as GCP
Strong programming fundamentals and experience writing clean, maintainable, and production-ready code
Excellent analytical and problem-solving skills — the ability to extract actionable insights from messy and high-volume data
Solid grasp of statistical testing, hypothesis validation, and common pitfalls in experimental design
Experience designing and interpreting A/B tests, including uplift measurement and segmentation
Ability to work closely with product and engineering teams to translate business goals into scalable ML or data solutions

Bonus points for:
Experience with reinforcement learning or sequence modeling techniques
Contributions to ML libraries, internal tools, or research publications