{"id":116893,"url":"https://alion.io/job/goto-group-senior-data-scientist-data-payments","title":"Senior Data Scientist - Data Payments","company":{"id":208,"name":"GoTo Group","domain":"gotocompany.com","url":"https://alion.io/company/goto-group","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":86,"open_postings":42,"ghost_share":0.143,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":80,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":54000,"max_usd":115000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":42},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-06-29T04:13:39Z","employer_posted_date":"2026-06-29","last_verified_at":"2026-10-23T16:13:58Z","board_verified":true,"closed_at":null,"days_open":116,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":116},"description":"About the Role\nDevelop and optimize personalization and recommendation models to deliver relevant offers, merchants, and experiences to GoPay users. Applya range of DS techniques -such as collaborative filtering, ranking models, embeddings, and graph-based methods to improve discovery and user engagement. Workclosely 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. Leveragelarge-scale behavioral and transactional data to uncover insights and guide feature design and personalization strategies. Contributeto 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.\nWhat You Will Do\nWork 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.\nRapidly prototype data science solutions and be involved in product and feature discussions\nAnalyze large volume data and generate insights which will be actionable.\nOwn end-to-end solutioning, from formulating the technical problem to deployment (along with engineers) of the solution\nParticipate in internal and external conferences and workshops.\nDesign and implement algorithms for search, recommendation, or advertising systems to drive discovery and conversion across Goto products.\nBuild 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.\nCollaborate with product, engineering, and business-facing data science teams to define problems, run experiments, and deploy solutions at scale.\nWhat You Will Need\n4-6years of relevant experience in applied data science or machine learning roles.\n\nProficiency in Python, and familiarity with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.\n\nSolid understanding ofMLfundamentals including supervised learning, ranking, embeddings, and evaluation metrics.\n\nHands-on experience in recommender systems, personalization, search, or ranking models is strongly preferred.\n\nExperience working with large-scale datasets using platforms such as Spark, MaxCompute (MC), or other distributed data environments.\n\nStrong analytical and problem-solving skills, with the ability to translate complex data insights into actionable recommendations.\n\nGood communication skills to engage with cross-functional partners and present results clearly.\n\nMaster’s or Ph.D. in a quantitative discipline (e.g., Computer Science, Statistics, Applied Mathematics, or related field) is a plus.\n\nHands-on experience with search/ recommendation/ads systems, with proven improvements to ranking, retrieval, or ad targeting models.\n\nFamiliarity with LLMs and/or LLVMs, with experience integrating them into search or recommendation pipelines a strong plus.\n\nDemonstrated 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\n\nStrong product intuition and ability to reason from user behavior data and traffic patterns.\n\nGood communication skills in English, both written and verbal.\n\nSelf-motivated, curious, and excited by the opportunity to build high-impact systems 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