{"id":113308,"url":"https://alion.io/job/iqvia-sr-data-science-consultant","title":"Sr Data Science Consultant","company":{"id":4918,"name":"IQVIA","domain":"iqvia.com","url":"https://alion.io/company/iqvia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":540,"ghost_share":0.006,"stale_share":0.798,"repost_share":0.03,"time_to_fill_p50_days":20,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["São Paulo, Brazil","Mexico City, Mexico"],"countries":["BR","MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":56000,"max_usd":119000,"period":"year","method":"role_seniority_country_cell","sample_n":8},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-08-19T00:00:00Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-22T23:23:34Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":64},"description":"The Team:\nOur Data Science / ML team is a global group working at the center of IQVIA's advances in machine learning and applied statistical methods for real-world evidence. Our work spans disease detection, patient outcome prediction, retrospective clinical studies, and patient subgroups discovery.\nWe are looking for a creative, intellectually curious Data Scientist / AIML Engineer to join our Healthcare Data Science team. This role supports both ML delivery and analytics delivery: you will develop ML solutions on large-scale medical data for disease detection and patient outcome prediction and translate client requirements for patient identification and HCP targeting into production analytics deliverables.\nA core part of the work is proving out new methodology and capabilities on live client delivery, then partnering with engineering and product to productionize and scale what works. You will also contribute to AI/ML enablement proofs of concept that enhance existing offerings or shape new ones for our healthcare and pharma clients.\nMust-have requirements:\n4-8 years ofrelevantexperience in Data Science / Machine Learning\nExperience in Healthcare, Pharma, or Life Sciences\nExperience developing machine learning algorithms and predictive models\nStrong knowledge of statistics and supervised ML methods (e.g., regression, XGBoost, SVM)\nStrong Python skills (including OOP) for ML and ETL pipelines\nExperience with large healthcare datasets (Claims, EMR, consumer data)\nExperience with Git and software development best practices\nExperience mentoring junior team members\nStrong communication and stakeholder management skills\nAbility to work independently and collaborate in cross-functional teams\nIQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com\nIQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. 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