Data Scientist (944) at ABC Supply Co. Inc. in Chicago, IL

Summary: ABC Supply Co. Inc. is North America’s largest wholesale distributor of exterior and interior building products. They are currently seeking a Data Scientist to deliver advanced analytics solutions and develop machine learning models that drive business value and improve operational efficiency. Responsibilities: - Develop machine learning and statistical models for a variety of use cases, including classification, clustering/segmentation, optimization, forecasting, and natural language processing - Wrangle, cleanse, and transform data from multiple sources to enable solution development - Carry out ad-hoc or targeted analyses to answer business or technical questions for a wide variety of stakeholders in the organization - Collaborate with project stakeholders to gather requirements, data, and other information necessary to achieve project goals, and to develop strategic solutions to ensure that project goals are achieved - Work closely with data engineers to design and build the data pipelines that feed analytic solutions - Actively participate in the software development lifecycle for data science-based applications, including design and development, testing, deployment, and support - Support the ongoing maintenance and enhancement of deployed models, including monitoring model performance and data drift - Leverage AI-powered coding and development tools (e.g., GitHub Copilot) to accelerate development workflows, improve code quality, and increase personal productivity - Create and maintain documentation using appropriate tools, including wikis for narrative documentation and version-controlled repositories for technical documentation - Research opportunities for new model usage and new uses for existing data - Stay current on advances in machine learning, statistical modeling, deep learning, and GenAI/LLM techniques and proactively identify opportunities to apply them - Operate in an agile environment intended to facilitate iterative development and continuous delivery - Effectively communicate with colleagues on both technical and non-technical topics, across a variety of communications platforms Required Qualifications: - Master's degree in a quantitative discipline (e.g., Statistics, Engineering, Sciences), or equivalent practical experience - 1-3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases, or statistical analysis - Demonstrable understanding and practical experience with a range of machine learning, statistical modeling, and/or operations research techniques, with depth in at least one core domain: supervised learning, time-series forecasting, clustering/segmentation, or optimization - Significant experience with common packages used for data wrangling (e.g., pandas, tidyverse), modeling (e.g., scikit-learn, caret/tidymodels), and data visualization (e.g., seaborn, matplotlib, ggplot2) - Understanding of descriptive and inferential statistics, e.g. hypothesis testing, confidence intervals, distributions, correlations - Understanding of the steps involved in the machine learning life cycle, including data selection and preparation, feature engineering, model training, model selection, model testing (cross-validation, A/B testing), model interpretation, and inference - Basic understanding of data management principles and architectures, including data warehouses, data lakes, and lakehouses, as well as the data integration (ETL/ELT) tools around them - Experience with at least one enterprise data visualization tool (Tableau preferred) - Experience with one or more cloud data platforms — Azure and Databricks preferred - Openness to adopting AI-powered development tools and assistants to enhance coding speed, debugging, and workflow efficiency - Enjoy working in a collaborative, team environment. Data science projects involve technical professionals from data engineering, DevOps, business intelligence, and business experts, and it is essential to develop and maintain positive relationships with these professionals - Strong communication skills, comfortable explaining technical concepts in common business terms to the business community and technical professionals from different backgrounds - Strong technical aptitude with a passion for continuous learning Preferred Qualifications: - Familiarity with experiment tracking and model management tools (e.g., MLflow) - Exposure to GenAI/LLM concepts: prompt engineering, retrieval-augmented generation (RAG), embeddings, or fine-tuning - Exposure to deep learning frameworks (e.g., PyTorch, TensorFlow) - Familiarity with operations research concepts such as linear programming, mixed-integer programming (MIP), or combinatorial optimization — applied to problems like vehicle routing, inventory optimization, or resource allocation - Experience with version control systems (Git) and collaborative development workflows - Understanding of MLOps principles: model deployment, monitoring, automated retraining - Familiarity with agile or Scrum methodologies Required Skills: Machine learning, Statistical modeling, Operations research, Python, R, SQL, pandas, tidyverse, scikit-learn, caret, tidymodels, seaborn, matplotlib, ggplot2, Descriptive statistics, Inferential statistics, Hypothesis testing, Confidence intervals, Distributions, Correlations, Machine learning life cycle, Feature engineering, Model training, Model selection, Model testing, Cross-validation, A/B testing, Model interpretation, Inference, Data warehouses, Data lakes, Lakehouses, ETL, ELT, Tableau, Azure, Databricks, MLflow, GenAI, LLM, Prompt engineering, Retrieval-augmented generation, Embeddings, Fine-tuning, PyTorch, TensorFlow, Linear programming, Mixed-integer programming, Combinatorial optimization, Vehicle routing, Inventory optimization, Resource allocation, Git, MLOps, Agile, Scrum Benefits: Health, dental, and vision coverage - eligible after 60 days, low out of pocket, 401(k) with generous company match - eligible after 60 days, immediately vested, Employer paid employee assistance program, Employer paid short term and long-term disability, Employer paid life insurance, Flex spending, Paid vacation, Paid sick days, Paid holidays