Staff Data ScientistThe 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.Working 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.ResponsibilitiesLead end-to-end data science projects, from problem framing and data exploration through model development, deployment, and monitoringDesign and build supervised, unsupervised, and deep learning models to solve high-impact business problemsDevelop AI agentic applications and LLM-powered solutions to automate workflows and unlock new capabilitiesDesign and analyze experiments (A/B tests, causal inference) to measure model and product impactPerform feature engineering, data validation, and quality assurance across large, complex datasetsPartner with data engineering and ML platform teams to productionize models and ensure scalability, reliability, and monitoringTranslate ambiguous business questions into well-scoped data science problems and communicate findings to technical and non-technical stakeholdersMentor junior data scientists and analysts, and contribute to best practices across the data science teamRequired Qualifications5–7 years of experience building and deploying production machine learning models, including supervised, unsupervised, and deep learning approachesAdvanced proficiency in Python and SQL, with deep experience in ML libraries such as scikit-learn, PyTorch or TensorFlow, pandas, and NumPyHands-on experience building AI agentic applications using LangChain, LangGraph, or similar frameworks, including integration with LLMs and external tools/APIsHands-on experience with the full ML lifecycle: feature engineering, model training, hyperparameter tuning, evaluation, deployment, and monitoringStrong foundation in statistics, experimental design (A/B testing), and core ML algorithms (gradient boosting, neural networks, clustering, dimensionality reduction)Experience working with large-scale datasets using distributed computing frameworks (Spark, Dask) and cloud platforms (AWS, Azure, or GCP)Proven ability to translate ambiguous business problems into technical solutions and communicate results to both technical and non-technical stakeholdersExperience mentoring junior data scientists in an Agile environmentPreferred QualificationsHands-on experience with Natural Language Processing (NLP) and/or computer vision applicationsFamiliarity with Large Language Models (LLMs), prompt engineering, and Generative AI techniquesExperience with MLOps practices and CI/CD pipelines for machine learning workflowsContributions to open-source projects