Lead Data Scientist - Data Payments

About the role

GoTo Financial is looking for a Lead Data Scientist to work on its GoPay payment app. The role develops and optimizes personalization and recommendation models so that users receive relevant offers, merchants and experiences. It works closely with product and engineering teams to design, test and deploy end-to-end ML pipelines in production, and runs A/B tests to measure model performance.

Key responsibilities

• Build data science solutions with engineers, product managers and business analysts to grow GoPay's customer base and transactions.
• Rapidly prototype solutions and take part in product and feature discussions.
• Analyze large volumes of data and turn them into actionable insights.
• Own end-to-end solutioning, from defining the technical problem to deployment with engineers.
• Take part in internal and external conferences and workshops.
• Design and implement algorithms for search, recommendation or advertising systems to drive discovery and conversion.
• Build advanced user behaviour models and query/assortment understanding models using LLMs and LLVMs to improve matching accuracy.
• Collaborate with product, engineering and business-facing teams to define problems, run experiments and deploy solutions at scale.

Required profile

• 6-8 years of experience in applied data science or machine learning roles.
• Master's or Ph.D. in a quantitative discipline (Computer Science, Statistics, Applied Mathematics or related) is a plus.
• Hands-on experience with search, recommendation or ads systems, with proven improvements to ranking, retrieval or ad targeting models.
• Familiarity with LLMs and/or LLVMs, and experience integrating them into search or recommendation pipelines, is a strong plus.
• Good written and spoken English and strong communication skills.

Required skills

• Proficiency in Python and familiarity with Scikit-learn, TensorFlow or PyTorch.
• Solid understanding of ML fundamentals: supervised learning, ranking, embeddings and evaluation metrics.
• Experience with large-scale datasets on platforms such as Spark, MaxCompute (MC) or other distributed data environments.
• Ability to translate complex data insights into actionable recommendations.