Senior Data Scientist - Data Payments

About the role

GoTo Financial is hiring a Senior Data Scientist for its GoPay Data Payments team. The role develops and optimizes personalization and recommendation models that deliver relevant offers, merchants and experiences to GoPay users, and works with product and engineering teams to design, test and deploy end-to-end ML pipelines in production.

Key responsibilities

• Apply techniques such as collaborative filtering, ranking models, embeddings and graph-based methods to improve discovery and user engagement.
• Prototype rapidly, run A/B tests and analyze online and offline metrics (CTR, conversion rate, NDCG, recall@k) to evaluate model performance.
• Leverage large-scale behavioral and transactional data to guide feature design and personalization strategies.
• Monitor model performance, identify data or algorithmic gaps and iterate based on real-world feedback.
• Build advanced user behaviour models and query/assortment understanding models using LLMs and LLVMs to improve matching accuracy across search and recommendation systems.
• Design and implement algorithms for search, recommendation or advertising systems, and take part in internal and external conferences and workshops.

Required profile

• 4-6 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.
• Proven hands-on experience with search, recommendation or ads systems, and with improving ranking, retrieval or ad targeting models.
• Good written and spoken English, strong analytical and problem-solving skills, and good communication with cross-functional partners.

Required skills

• Proficiency in Python; 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 Spark, MaxCompute (MC) or similar distributed platforms.
• Familiarity with LLMs and/or LLVMs, and experience integrating them into search or recommendation pipelines is a strong plus.