{"id":3969167,"url":"https://alion.io/job/cognizant-ml-applied-scientist","title":"ML / Applied Scientist","company":{"id":85,"name":"Cognizant","domain":"cognizant.com","url":"https://alion.io/company/cognizant","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Cognizant Careers","truth_index":{"grade":"A","score":87,"open_postings":178,"ghost_share":0,"stale_share":0.489,"repost_share":0.056,"time_to_fill_p50_days":23,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Phoenix, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":115000,"max":130000,"currency":"USD","period":"year","gross":null,"usd_annual":130000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest-Assured","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-11-09T08:35:47Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T15:24:33Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the role:\nAs an ML / Applied Scientist, you will make an impact by developing machine learning models that power entity resolution, inference, and intelligent data classification across enterprise data ecosystems. You will be a valued member of the AI and Data Engineering team, working closely with Architects, Data Domain SMEs, Backend Engineers, and platform stakeholders to build scalable AI solutions that improve data quality and business intelligence.\nIn this role, you will:\nDesign and develop entity matching models that combine fuzzy and exact matching techniques to create and maintain trusted golden records.\n\nBuild machine learning models that identify patterns, relationships, and anomalies across complex enterprise datasets to support data profiling and business insights.\n\nDevelop AI-powered labeling and classification solutions using Gemini and other large language model technologies to support business ontology initiatives.\n\nDeploy, monitor, evaluate, and continuously improve machine learning models on Vertex AI, ensuring high levels of accuracy, performance, and reliability.\n\nPartner with cross-functional teams to integrate AI capabilities into enterprise platforms, workflows, and agent orchestration solutions.\n\nWork model:\nWe strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a remote position open to qualified applicants in the United States. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.\nThe working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.\nWhat you need to have to be considered:\n5+ years of experience in applied machine learning, with expertise in entity resolution, classification, NLP, or matching systems.\n\nStrong programming skills in Python and hands-on experience with machine learning frameworks such as PyTorch and/or scikit-learn.\n\nExperience deploying, monitoring, and evaluating machine learning models in Vertex AI or similar cloud ML platforms.\n\nExperience working with embeddings, large language models (LLMs), and NLP techniques for classification, search, matching, or inference use cases.\n\nProven experience designing labeling and training workflows using weak supervision, human-in-the-loop review processes, and AI-assisted labeling approaches.\n\nThese will help you stand out:\nExperience working with knowledge graphs, ontologies, or graph-based machine learning applications.\n\nUnderstanding of MLOps practices, including model versioning, monitoring, drift detection, and automated retraining pipelines.\n\nExperience building enterprise AI solutions using Google Cloud Platform services, including BigQuery and Vertex AI.\n\nFamiliarity with Gemini APIs and applying generative AI technologies in production environments.\n\nGoogle Cloud Professional Machine Learning Engineer certification.\n\nWe're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.\nSalary and Other Compensation:\nApplications will be accepted until November 29, 2026\nThe annual salary for this position is between $ 115,000 - $ 130,000 depending on experience and other qualifications of the successful candidate.\nThis position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.\nBenefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:\nMedical/Dental/Vision/Life Insurance\n\nPaid holidays plus Paid Time Off\n\n401(k) plan and contributions\n\nLong-term/Short-term Disability\n\nPaid Parental Leave\n\nEmployee Stock Purchase Plan\n\nDisclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. 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