Principal Data Scientist

Role Overview

We’re seeking a Principal Data Scientist to lead a high-impact analytics organization focused on real-world business problems across merchandising, supply chain, sales, service, and customer-facing operations. This role is less about infrastructure build and more about shaping our AI/ML roadmap, delivering predictive and prescriptive models, translating business needs into analytics solutions, and driving measurable value across the enterprise. The Principal Data Scientist will work cross-functionally with merchandising, marketing, operations, finance, and IT to embed insights into decisions, accelerate value capture, and act as a strategic partner to the senior leadership team.

Key Responsibilities

• Develop and define the vision, strategy, and roadmap for advanced analytics and AI/ML across the company, aligned with company goals (e.g., growth, retention, margin, customer satisfaction).
• Lead and mentor a team of data scientists, applied ML engineers, and analytics specialists to deliver models, experiments, and analytics workflows that drive measurable business outcomes.
• Partner with business stakeholders (merchandising, supply chain, sales, operations) to identify high-value opportunities, frame analytic questions, design solutions, and quantify value (e.g., cost reduction, revenue uplift, service throughput).
• Drive the full model lifecycle: problem definition, data exploration, feature engineering, model development, evaluation, deployment (through partner channels), monitoring and iteration.
• Own the metrics and insights that matter: customer behavior prediction (churn, cross-sell, lifetime value), price optimization, parts demand forecasting, supply-chain disruption prediction, service center performance analytics, etc.
• Translate complex analytical findings into clear, persuasive business narratives and decision-ready dashboards/presentations for senior leadership.
• Stay abreast of AI/ML advances (e.g., AI/ML, large language models, causal inference, reinforcement learning) and pilot/introduce new methods where they create differentiated advantage.
• Collaborate with IT and data engineering to ensure analytics access and data quality.
• Establish best practices around model governance, interpretability, fairness, and ROI tracking; ensure responsible, ethical AI usage.
• Define talent needs, recruit top-tier data science professionals, build a world-class analytics culture, and retain high-performing team members.

Ideal Candidate Profile

• 10 years of experience in analytics or data science, with at least 3-5 years in a leadership role (managing data science teams).
• Strong track record of translating business problems into analytics/AI solutions and delivering measurable impact (e.g., revenue uplift, cost savings, new products).
• Experience in retail, distribution, supply chain, automotive aftermarket, or similar operations-intensive business is a plus.
• Solid hands-on experience with machine learning techniques (supervised/unsupervised learning, time-series, causal inference, NLP) and a strong understanding of when/why to use them.
• Comfortable making design decisions around model architecture, feature engineering, evaluation metrics, and deployment strategy.
• Excellent communication and influencing skills - able to engage senior leadership, translate technical concepts into business value, build cross-functional buy-in.
• With a data-driven mindset and curiosity to explore new methods, frameworks, and tools (e.g., Python, R, TensorFlow, PyTorch, SQL).
• Demonstrated ability to hire, develop, and retain high-performing analytics teams and create a collaborative, accountable culture.
• Master’s or PhD in a quantitative field (Analytics, Statistics, Computer Science, Engineering, Applied Math) preferred.