Staff AI/ML Engineer & Data Scientist

Job Title: Staff AI/ML Engineer & Data Scientist Schedule: 9 AM - 6 PM Central Time, M-F Location: Remote, with approximately one 3-day trip per month to Normal, IL (expenses paid) for the first 3 months. Role Summary We are seeking a Staff AI/ML Engineer with deep expertise in traditional machine learning and strong MLOps skills to lead the design and deployment of production-grade ML systems. The primary focus is on building scalable, explainable models using traditional and time-series techniques, with a strong emphasis on end-to-end pipeline architecture in Databricks and AWS. Core Responsibilities & Skills • End-to-End ML Ownership: Lead the full model lifecycle—from data preprocessing and feature engineering to training, deployment, and monitoring—ensuring models are production-ready. • Traditional ML Mastery: Apply and tune algorithms like regression, tree-based models, SVMs, and clustering to solve problems, primarily with unlabeled data (e.g., anomaly prediction). • MLOps & DevOps Engineering: Architect and manage robust CI/CD pipelines for ML. Must have hands-on experience with Databricks, MLflow, AWS, database setup, and model operationalization. • Statistical Rigor: Apply hypothesis testing, Bayesian methods, and interpretability techniques to validate models and ensure reliable insights. • Domain Application: Analyze manufacturing, sensor, and PLC data to derive high-impact business solutions. Must-Have Qualifications • Master's or PhD is mandatory. • 8+ years in applied ML/Data Science, including 3+ years in a senior/staff-level role. • Expert proficiency in Python (Pandas, NumPy, Scikit-learn, XGBoost) and proven experience deploying traditional ML models to production. • Hands-on MLOps experience with MLflow and Databricks (highly preferred), including model monitoring, drift detection, and automated retraining. • Strong DevOps experience, including CI/CD and database skills (SQL/NoSQL). Preferred Qualifications • Exposure to RAG pipelines and vector databases. • Experience with time-series analysis, anomaly detection, and cloud platforms (AWS, Azure, GCP). • Familiarity with distributed computing (Spark, Ray). Soft Skills • Strategic problem-solver who can align technical solutions with business goals. • Excellent communicator, able to engage effectively with both technical and non-technical stakeholders.