Senior Principal Data Scientist

Meesho; Bengaluru; On-site; INR 3,000,000 - 4,000,000; Full time

Job summary

Meesho is seeking a Senior Principal Data Scientist in Bengaluru, Karnataka to lead a team of data scientists in building intelligent systems that impact millions of users. The role involves owning the data science roadmap, mentoring the team, and collaborating with product and engineering leaders. Applicants should have over 10 years of experience in Data Science with a Master’s degree (PhD preferred) and strong coding skills in Python and SQL. This position offers an opportunity to work on large-scale ML solutions in a dynamic environment.

Qualifications

• 10+ years of experience in Data Science or Analytics, including 2–3 years in people management.
• Proven track record of building and deploying ML models in production.
• Experience managing teams of 10+ data scientists.

Responsibilities

• Lead, grow, and mentor a high-performing team of data scientists.
• Own all data science systems and models in your charter.
• Collaborate with leaders to define and prioritize DS initiatives.

Skills

Machine Learning

Statistics

Python

SQL

Data Science

Stakeholder Management

Education

Master’s degree in Machine Learning, Statistics, Computer Science or related field

PhD preferred

Tools

Spark

Hive

Redshift

Job description

About the role

Are you a strategic thinker with a passion for solving large-scale problems using data? Do you enjoy mentoring high-performing teams and building ML systems that directly impact millions of users? We’re looking for a Senior Principal Data Scientist to lead a team of skilled data scientists in building intelligent systems that power Meesho’s next phase of growth.

In this role, you’ll own the data science roadmap for a key business charter, guiding the team through ambiguity and complexity to deliver production-grade ML solutions. You’ll work closely with product, tech, and business leaders to translate complex challenges into scalable, measurable, and impactful outcomes. As a people and technical leader, you’ll ensure model efficiency, system reliability, and scientific excellence while fostering a culture of innovation, collaboration, and continuous improvement.

What You Will Do

• Lead, grow, and mentor a high-performing team of data scientists.

• Own all data science systems and models in your charter — from strategy to deployment and monitoring.

• Collaborate with senior product, engineering, and business leaders to define and prioritize impactful DS initiatives.

• Drive platformization, system architecture decisions, and ML lifecycle improvements across the charter.

• Ensure model scalability, performance, and cost efficiency, while upholding best practices in experimentation and statistical rigor.

• Guide the team in reading and implementing state-of-the‑art research, and facilitate build vs. buy decisions.

• Lead RCA for critical production issues and improve system observability, documentation, and service uptime.

What You Will Need

• Master’s degree (PhD preferred) in Machine Learning, Statistics, Computer Science, or a related quantitative field.

• 10+ years of experience in Data Science or Analytics, with at least 2–3 years of people management experience.

• Proven track record of building and deploying ML models in production at scale.

• Experience managing teams of 10+ data scientists and delivering across cross‑functional charters.

• Deep expertise in ML algorithms, experimental design, and performance monitoring.

• Strong coding skills (Python, SQL) and familiarity with Big Data technologies like Spark, Hive, or Redshift.

• Ability to translate business needs into technical solutions, prioritize roadmaps, and estimate effort accurately.

• Strong communication and stakeholder management skills with a bias for action and clarity in execution.

Bonus Points For

• Experience leading ML initiatives in B2C or e-commerce settings.

• Contributions to internal tools, open-source libraries, or research publications.

• Experience in personalization, recommendations, ranking, or supply chain ML problems.

• Strong understanding of ML system performance trade‑offs — latency, cost, and throughput.