{"id":3631569,"url":"https://alion.io/job/mazzatech-3693-sr-data-science","title":"3693: Sr. Data Science","company":{"id":4495779,"name":"Mazzatech","domain":"mazza.tech","url":"https://alion.io/company/mazzatech-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"InHire","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["BR"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"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},{"name":"AI Agents","optional":true},{"name":"Databricks","optional":true},{"name":"Fine-tuning","optional":true},{"name":"NLP","optional":true},{"name":"pySpark","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-23T20:37:54Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-11-07T09:48:50Z","board_verified":true,"closed_at":null,"days_open":14,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":14},"description":"Sr. Consultant / Data Science || Relevant work experience: 4-8 Years\nThe Team:\nThe Data Science / ML team is a global group working, 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.\nIdeally you will have:\nExperience working on creating machine learning algorithms in the healthcare and/or commercial Pharma industries\nExperience working with large, real world datasets including US open medical claims, ambulatory EMR, and consumer data.\nExperience mentoring junior machine learning engineers\nKnowledge of supervised machine learning methods, such as regularized regressions, ensemble tree classifiers (e.g. XGBoost), support vector machines, etc. Good understanding of classical statistical methods, such as fitting regression models, inference testing and sampling.\nProficiency in Python, including OOP, to support ML and ETL pipelines\nUnderstanding coding practices and version control software such as Git, ability to write clean and efficient code, and a good understanding of the Data Science package landscape.\nExcellent communication skills, both verbal and written, including the ability to present technical concepts to a lay audience, write analysis plans for projects, contribute to proposals, represent ideas effectively and persuasively to clients / internal stakeholders, etc.\nA proactive, innovative and pragmatic approach to problem solving and the ability to think critically and independently, able to work as part of a cross-functional team.\nBonus points for:\nExperience with high volume data sets that benefit from parallel computation, such as with Pyspark/ Spark\nKnowledge of healthcare / life science issues involving Real-World Evidence.\nExperience with production pipelines that power recurring client deliverables at cadence.\nExperience translating epidemiologic, clinical, or commercial rule sets into scalable code on claims or EMR data.\nExperience developing agentic solutions to workflow automation and data-driven insights\nExperience with Databricks and/or Snowflake 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