{"id":2830182,"url":"https://alion.io/job/recruitis-staff-principal-data-scientist","title":"Staff-Principal Data Scientist","company":{"id":688309,"name":"Recruitis","domain":"recruitis.io","url":"https://alion.io/company/recruitis","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"D","score":52,"open_postings":10,"ghost_share":0.8,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bangkok, Thailand"],"countries":["TH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":47000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":709},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false}],"status":"live","first_seen_at":"2026-03-06T06:09:37Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-24T21:15:58Z","board_verified":true,"closed_at":null,"days_open":232,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":231},"description":"Who is our client?\nOur client is a large-scale technology organization with a strong focus on engineering, data, and AI. They are continuing to invest in AI/ML platform capabilities that enable teams to build, operate, and scale machine learning and intelligent applications more effectively.\nAbout the Role\nThis role is for a senior individual contributor who will lead the design and improvement of machine learning-powered solutions in a production environment. You will work on complex user-facing AI systems, drive experimentation and model improvement, and collaborate closely with cross-functional teams to turn ambiguous problems into practical, scalable solutions.\nResponsibilities\nLead the development and enhancement of machine learning-based solutions for complex, real-world use cases\nTranslate open-ended product or user problems into clear data science approaches, experiments, and deliverable outcomes\nDesign, test, and refine prompts, retrieval strategies, evaluation frameworks, and model behaviors to improve quality and reliability\nBuild robust experimentation methods to assess model performance and compare alternative approaches\nWork closely with product, engineering, analytics, and other technical teams to define priorities and drive implementation\nTake ownership of solution quality across the lifecycle, from early exploration through deployment and iteration\nUse data-driven methods to identify opportunities for model improvement, user impact, and system optimization\nContribute to the design of scalable workflows, including evaluation pipelines, monitoring approaches, and feedback loops\nCommunicate findings, trade-offs, and recommendations clearly to both technical and non-technical stakeholders\nSupport strategic decisions on model quality, scalability, performance, and long-term maintainability\nProvide senior-level guidance in solving ambiguous AI problems with practical and measurable outcomes\nQualifications\nProven experience in a Staff Data Scientist, Applied Scientist, ML Engineer, or similar role, with strong ownership of end-to-end ML or LLM initiatives\nStrong hands-on experience with machine learning platforms in production settings, including experimentation, evaluation, iteration, and quality improvement\nSolid background in machine learning, statistics, and data science fundamentals\nExperience applying quantitative methods to solve complex product or system problems at scale\nStrong coding ability and practical experience working closely with software engineering teams in production environments\nExperience building or improving systems that involve prompt design, retrieval, ranking, response quality assessment, or model evaluation\nStrong understanding of experimentation design, success metrics, offline and online evaluation, and performance analysis\nComfortable working with ambiguity and shaping loosely defined problems into structured analytical approaches\nAble to communicate complex technical ideas clearly and influence cross-functional stakeholders\nExperience operating in fast-paced, product-driven environments with high expectations for quality and execution\nAdvanced degree in a relevant field such as Computer Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative discipline is 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