Job description:

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 Masters is a plus

- 4 - 9 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