onsite
Lead Data Scientist
Lead Data Scientist
The Lead Data Scientist will own and deliver solutions across multiple charters, mentoring a team of data scientists, and optimizing user journeys through data analysis. This role involves building scalable ML pipelines, improving model performance, and leading experimental design, while also fostering academic collaboration and publishing research.
About the role
About the Role
As a Lead Data Scientist, you will be responsible for owning and delivering solutions across multiple charters, formulating well-scoped problem statements, and driving them to execution with measurable impact.
Responsibilities
- 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.
Skills
Machine LearningBig DataMl Pipelinesexperimental designstatistical rigorData Mining