Original listing text, shown exactly as published by the company.
About the Role
Develop and optimize personalization and recommendation models to deliver relevant offers, merchants, and experiences to GoPay users. Apply a range of DS techniques—such as collaborative filtering, ranking models, embeddings, and graph-based methods to improve discovery and user engagement. Work closely with product and engineering teams to design, test, and deploy end-to-end ML pipelines in production. Prototype rapidly, run A/B tests, and analyze online/offline metrics (CTR, conversion rate, NDCG, recall@k, etc.) to evaluate model performance and business impact. Leverage large-scale behavioral and transactional data to uncover insights and guide feature design and personalization strategies. Contribute to continuous improvement by monitoring model performance, identifying data or algorithmic gaps, and iterating based on real-world feedback. Collaborate cross-functionally with other data scientists and engineers to share best practices, improve our personalization infrastructure, and align with GoTo’s broader personalization roadmap.
What You Will Do
- Work as part of a cross-functional team of engineers, product managers and business analysts to build data science solutions that build customer engagement and help grow GoPay’s customer base and transactions.
- Rapidly prototype data science solutions and be involved in product and feature discussions
- Analyze large volume data and generate insights which will be actionable.
- Own end-to-end solutioning, from formulating the technical problem to deployment (along with engineers) of the solution
- Participate in internal and external conferences and workshops.
- Design and implement algorithms for search, recommendation, or advertising systems to drive discovery and conversion across Goto products.
- Build advanced user behaviour models and query/assortment understanding models using LLMs and LLVMs to improve matching accuracy between users, queries, and items across search and recommendation systems.
- Collaborate with product, engineering, and business-facing data science teams to define problems, run experiments, and deploy solutions at scale.
What You Will Need
- 6–8 years of relevant experience in applied data science or machine learning roles.
- Proficiency in Python, and familiarity with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.
- Solid understanding of ML fundamentals including supervised learning, ranking, embeddings, and evaluation metrics.
- Experience working with large-scale datasets using platforms such as Spark, MaxCompute (MC), or other distributed data environments.
- Strong analytical and problem-solving skills, with the ability to translate complex data insights into actionable recommendations.
- Good communication skills to engage with cross-functional partners and present results clearly.
- Master’s or Ph.D. in a quantitative discipline (e.g., Computer Science, Statistics, Applied Mathematics, or related field) is a plus.
- Hands-on experience with search/ recommendation/ads systems, with proven improvements to ranking, retrieval, or ad targeting models.
- Familiarity with LLMs and/or LLVMs, with experience integrating them into search or recommendation pipelines a strong plus.
- Demonstrated ability to innovate with new algorithms or tools and drive measurable impact, especially making use of Large language Models (LLMs) and Large Language and Vision models (LLVMs) in search modeling or recommendation modeling
- Strong product intuition and ability to reason from user behavior data and traffic patterns.
- Good communication skills in English, both written and verbal.
- Self-motivated, curious, and excited by the opportunity to build high-impact systems quickly.
