About the Role

We're seeking a Staff Data Scientist to develop and deploy machine learning solutions for consumer growth, including fraud detection, pricing optimization, and user behavior modeling. You'll combine strong technical skills with a passion for learning to build scalable ML systems that drive business impact.

This role requires a blend of strong research skills, mathematical rigor, and production-grade engineering practices. You will also act as a mentor to junior scientists, collaborate cross-functionally with product, engineering, and business stakeholders, and advance the company’s research agenda through thought leadership and, when possible, publications in top-tier venues.

Key Responsibilities

• End-to-End Delivery: Build complete ML pipelines from data processing to model deployment and monitoring in production environments

• ML Development: Design and implement machine learning models for growth applications, from classical algorithms to deep learning approaches

• Cross-Functional Collaboration: Work closely with product, engineering, and senior researchers to translate requirements into effective ML solutions

• Mentorship: Guide junior data scientists and contribute to team knowledge sharing

• Research & Innovation: Stay current with ML research and explore new methodologies relevant to consumer growth challenges

Required Qualifications

• Education: PhD/MS in Machine Learning, Data Science, or closely related field

• Experience: 3+ years (PhD) or 5+ years (MS) of hands-on machine learning experience

• Technical Skills: 

• Proficient in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn, XGBoost)

• Experience with SQL and large-scale data processing

• Proven track record deploying ML models to production

• Mindset: Strong desire to learn new techniques and adapt to evolving business needs

• Communication: Ability to clearly articulate ML concepts to diverse stakeholders

Preferred Qualifications

• Experience in consumer-facing applications (growth, fraud, personalization)

• Familiarity with distributed computing systems (Spark, Ray)

• Publications in ML conferences or contributions to open-source projects