Principal Data Scientist

• Lead a team of applied scientists developing AI-driven solutions
• Build and ship GenAI applications using agentic frameworks
• Automate merchant and operator workflows
• Design and deploy models and agents to investigate anomalies across merchandising, supply chain, and store operations
• Surface the causes behind business signals
• Research and integrate techniques across LLMs, agents, retrieval, and classical machine learning
• Mentor scientists and engineers and set the team's technical bar
• Partner with product, engineering, and business stakeholders to translate ambiguous problems into high-impact AI solutions

Requirements

• 7+ years of Applied Scientist / MLE experience with a strong track record of shipping ML models to production in high-scale environments
• Deep core ML/AI fundamentals, including supervised learning, deep learning, time-series, and causal/RCA
• Hands-on GenAI experience with LLMs, fine-tuning, evaluations, and agent orchestration
• Production-grade Python
• Distributed systems
• ML pipelines
• CI/CD for ML
• Model monitoring
• Experience with large-scale data systems such as Spark/Hive
• Experience with cloud platforms such as GCP/Azure
• Leadership in mentoring teams and guiding technical development aligned with business objectives
• Bachelor's degree in a specified or related field and 5 years' analytics experience; or Master's degree in a specified or related field and 3 years' analytics experience; or 7 years' experience in an analytics or related field
• Drug-free workplace policy compliance

Core Competencies

Demonstrates expertise in developing AI-driven solutions, with a strong focus on GenAI applications, machine learning fundamentals, and leadership in mentoring teams. Proficient in deploying models and automating workflows to enhance business operations and decision-making.

Highest-signal resume keywords

• Applied Scientist Experience
• GenAI Applications Development
• Machine Learning Fundamentals
• Production-Grade Python
• Cloud Platforms Experience

Hard Skills

• Machine Learning
• Deep Learning
• Supervised Learning
• Time-Series Analysis
• Causal Analysis
• Model Monitoring
• ML Pipelines
• CI/CD for ML
• Agent Orchestration
• Anomaly Detection

Soft Skills

• Mentoring
• Leadership
• Collaboration
• Problem-Solving

Industry Keywords

• AI Solutions
• High-Scale Environments
• Business Signals
• Analytics Experience
• Drug-Free Workplace Policy

Tools & Technologies

• GCP
• Azure
• Spark
• Hive
• Distributed Systems