{"id":2944812,"url":"https://alion.io/job/the-vanguard-group-aiml-scientist","title":"AI/ML Scientist","company":{"id":253,"name":"The Vanguard Group","domain":"vanguard.com","url":"https://alion.io/company/vanguard","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":81,"open_postings":85,"ghost_share":0.059,"stale_share":0.506,"repost_share":0.212,"time_to_fill_p50_days":27,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Malvern, United States","Charlotte, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":96000,"max_usd":214000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":370},"experience_years_min":1,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-20T18:28:27Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-22T02:34:46Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"What You’ll Do\nIdentify high-value AI/ML use cases with stakeholders and translate business needs into solution approaches, success metrics, and delivery plans\nDevelop and support AI/ML solutions using machine learning, NLP, Generative AI, and LLM-based approaches such as retrieval-augmented generation and agentic workflow automation\nCollaborate across data science and engineering teams to deploy scalable solutions with appropriate evaluation, monitoring, guardrails, and alignment to enterprise risk and responsible AI standards\nCommunicate technical concepts clearly to technical and non-technical stakeholders\nRequired Qualifications\nUndergraduate degree or equivalent experience in Computer Science, Statistics, Machine Learning, Data Science, Electrical Engineering, or a related field; graduate degree preferred\n1 to 2 years of experience developing and implementing AI/ML solutions or projects, with experience applying machine learning, LLMs, NLP, and data science techniques to business problems\nStrong foundation in machine learning and statistical methods, including regression, clustering, decision trees, neural networks, deep learning, and supervised and unsupervised learning\nExperience with tools such as TensorFlow, PyTorch, scikit-learn, NLP libraries, or similar technologies, plus strong technical knowledge of cloud, big data, scalable data processing, SQL, and NoSQL databases\nStrong communication skills and ability to work effectively in cross-functional environments\nWhat Success Looks Like\nAI/ML solutions deliver measurable business value in real workflows\nLLM and Agentic capabilities are applied to practical use cases\nBusiness partners see you as a strong problem solver and collaborative technical partner\nSpecial Factors\nSponsorship\nVanguard is not offering visa sponsorship for this position.About Vanguard\nAt Vanguard, we don't just have a mission-we're on a mission.\nTo work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.\nHow We Work\nVanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. 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