Lead Applied AI/ML Data Scientist

About this role

About Dynatron Dynatron is transforming the automotive service industry with intelligent SaaS solutions that drive measurable results for thousands of dealership service departments. Our analytics, automation, and AI-powered workflows help service leaders improve profitability, increase operational efficiency, and make smarter business decisions. As Dynatron expands its AI capabilities, we're focused on building intelligence that solves meaningful customer problems—not adding AI for its own sake. That requires exceptional applied data science, rigorous evaluation, strong product judgment, and the ability to turn complex automotive data into capabilities that perform reliably in the real world. The Opportunity We're looking for a Lead Applied AI/ML Data Scientist to serve as a technical authority for the AI and machine learning capabilities embedded within Dynatron's SaaS platform. This is a senior, hands-on individual contributor role for someone who has built AI capabilities that reached production, served real customers, and evolved based on what happened after launch. You'll own modeling approaches across core prediction and classification use cases while helping define how Dynatron evaluates, prioritizes, and develops emerging generative AI capabilities. You'll work directly with Product Managers, Product Owners, Engineering, and product leadership to translate business problems into technically sound AI solutions. Just as importantly, you'll help determine which ideas shouldn't be built: challenging assumptions, identifying limitations, and recommending better approaches when the technology doesn't support the desired outcome. Proofs of concept aren't the finish line here. Success means building AI capabilities that create measurable value for customers and perform reliably in production. What You'll Do Lead Applied Machine Learning Own modeling approaches for Dynatron's core classification, prediction, and other applied machine learning use cases. Design features and modeling strategies for complex, messy, real-world automotive data. Establish rigorous approaches to class imbalance, validation, experimentation, and model evaluation. Continuously improve models based on production performance, changing data, and customer outcomes. Raise the standard for how applied machine learning is developed, evaluated, documented, and shipped across the organization. Build Production AI Capabilities Design and build AI/ML capabilities from initial problem definition through production release. Translate customer and product problems into appropriate modeling approaches rather than beginning with a predetermined technology. Build solutions that balance model quality, scalability, explainability, latency, cost, and maintainability. Remain engaged after launch to understand real-world performance and improve capabilities based on production evidence. Shape the AI Product Roadmap Serve as a technical authority on the feasibility of proposed AI capabilities. Partner with Product leadership to evaluate opportunities before significant engineering investment is made. Clearly articulate what is technically achievable, what requires additional data or sequencing, and what is unlikely to deliver the intended result. Recommend alternative approaches when AI isn't the appropriate solution. Help prioritize opportunities based on customer value, technical feasibility, data readiness, and implementation complexity. Build with Generative AI & Agentic Systems Develop production capabilities using LLMs and modern agentic frameworks where they provide meaningful product value. Design retrieval architectures, tool-use patterns, and other approaches for grounding AI systems in Dynatron's proprietary data. Evaluate and adapt foundation models for domain-specific applications, including fine-tuning where appropriate. Establish appropriate controls around quality, latency, token usage, and cost per interaction. Stay current with emerging AI capabilities whil

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