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