{"id":2809064,"url":"https://alion.io/job/rb-global-staff-data-scientist","title":"Staff Data Scientist","company":{"id":2965322,"name":"RB Global","domain":"rbglobal.com","url":"https://alion.io/company/rbglobal","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Westchester, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":134000,"max_usd":267000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":414},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Dask","optional":false},{"name":"GCP","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"CI/CD","optional":true},{"name":"Computer Vision","optional":true},{"name":"NLP","optional":true},{"name":"Prompt Engineering","optional":true}],"status":"live","first_seen_at":"2026-04-28T19:32:35Z","employer_posted_date":"2026-04-28","last_verified_at":"2026-10-18T08:38:40Z","board_verified":true,"closed_at":null,"days_open":172,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":172},"description":"About the Role\nThe Staff Data Scientist will play a key role in advancing IAA’s Machine Learning (ML) and advanced analytics initiatives. This role involves building supervised and unsupervised ML models, as well as developing AI agentic applications, with a strong business orientation and a track record of delivering data-driven services. The Staff Data Scientist will implement scalable, machine learning-based solutions to drive growth and operational efficiency across the organization.\nWorking closely with cross-functional teams within the IAA ecosystem, the Staff Data Scientist will help integrate these solutions into enterprise-scale services. This role requires deep knowledge of data best practices to ensure the most appropriate data sources are leveraged to answer business questions and perform advanced analysis of customer behavior, vehicle listings, and other business data.\nResponsibilities\nLead end-to-end data science projects, from problem framing and data exploration through model development, deployment, and monitoring\nDesign and build supervised, unsupervised, and deep learning models to solve high-impact business problems\nDevelop AI agentic applications and LLM-powered solutions to automate workflows and unlock new capabilities\nDesign and analyze experiments (A/B tests, causal inference) to measure model and product impact\nPerform feature engineering, data validation, and quality assurance across large, complex datasets\nPartner with data engineering and ML platform teams to productionize models and ensure scalability, reliability, and monitoring\nTranslate ambiguous business questions into well-scoped data science problems and communicate findings to technical and non-technical stakeholders\nMentor junior data scientists and analysts, and contribute to best practices across the data science team\nRequired Qualifications\n5-7 years of experience building and deploying production machine learning models, including supervised, unsupervised, and deep learning approaches\nAdvanced proficiency in Python and SQL, with deep experience in ML libraries such as scikit-learn, PyTorch or TensorFlow, pandas, and NumPy\nHands-on experience building AI agentic applications using LangChain, LangGraph, or similar frameworks, including integration with LLMs and external tools/APIs\nHands-on experience with the full ML lifecycle: feature engineering, model training, hyperparameter tuning, evaluation, deployment, and monitoring\nStrong foundation in statistics, experimental design (A/B testing), and core ML algorithms (gradient boosting, neural networks, clustering, dimensionality reduction)\nExperience working with large-scale datasets using distributed computing frameworks (Spark, Dask) and cloud platforms (AWS, Azure, or GCP)\nProven ability to translate ambiguous business problems into technical solutions and communicate results to both technical and non-technical stakeholders\nExperience mentoring junior data scientists in an Agile environment\nPreferred Qualifications\nHands-on experience with Natural Language Processing (NLP) and/or computer vision applications\nFamiliarity with Large Language Models (LLMs), prompt engineering, and Generative AI techniques\nExperience with MLOps practices and CI/CD pipelines for machine learning workflows\nContributions to open-source 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