Lead Applied AI/ML Data Scientist

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 while applying disciplined judgment about where they belong in production.

Define AI Evaluation Standards

• Establish rigorous evaluation methodologies for traditional ML and non-deterministic generative AI systems.
• Define appropriate offline and production metrics for individual use cases.
• Design evaluation frameworks that measure accuracy, reliability, business usefulness, and other relevant quality dimensions.
• Monitor production performance and use real-world results to guide model improvement.
• Help establish consistent standards for determining when an AI capability is ready for customers.

Partner Through Production

• Work closely with Engineering and MLOps/DevOps partners to establish production-readiness criteria.
• Define requirements for deployment, monitoring, retraining, and model lifecycle management.
• Ensure appropriate handoffs without treating productionization as someone else's problem.
• Collaborate across Data Engineering, Product, and Engineering to ensure AI solutions have the data and infrastructure required to perform reliably.

What You Bring

Applied Data Science & Machine Learning Expertise

• 10+ years of experience in Data Science, Applied Machine Learning, or a closely related discipline.
• Deep expertise in traditional machine learning, including classification, feature engineering, class imbalance, and model evaluation.
• Significant experience working with complex, imperfect real-world datasets rather than exclusively curated research data.
• Strong understanding of experimental design and how to determine whether a model is actually improving an outcome.

Production AI Experience — Critical

• Demonstrated experience shipping AI/ML capabilities into commercial products used by real customers.
• Ability to speak specifically about systems you've built, their scale, how they performed after release, and what you changed based on production evidence.
• Experience supporting and improving models throughout their production lifecycle.
• Strong understanding of the differences between building a successful prototype and operating a successful AI product.

Generative AI & LLM Expertise

• Production experience building with LLMs and agentic frameworks.
• Experience designing retrieval architectures and grounded AI applications.
• Strong understanding of evaluation methodologies for non-deterministic systems.
• Experience managing quality, latency, token consumption, and cost per interaction in production.
• Experience fine-tuning or otherwise adapting transformer models for domain-specific use cases.

Product & Business Judgment

• Demonstrated experience scoping AI initiatives directly with Product Managers and business stakeholders.
• Track record of identifying technically weak or commercially impractical AI concepts and influencing stakeholders toward better solutions.
• Ability to translate business problems into modeling problems—and recognize when the underlying problem doesn't require AI.
• Strong customer orientation with curiosity about the business problem behind the requested capability.

Technical Skills

• Expert-level Python and strong SQL skills.
• Comfortable working directly with large datasets in cloud data warehouse environments.
• Experience collaborating within modern cloud-based data and ML ecosystems.
• Strong understanding of the data requirements and dependencies necessary to support production AI.

Communication & Technical Leadership

• Ability to communicate sophisticated AI concepts, limitations, and trade-offs clearly to non-technical stakeholders.
• Strong influence skills and confidence challenging assumptions constructively.
• Ability to establish technical standards and raise the quality of work without direct people-management authority.
• Strong documentation habits and commitment to making technical decisions understandable and reproducible.

Education

• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or another quantitative discipline, or equivalent practical experience.

Nice to Have

• Experience working with automotive, dealership, or Fixed Operations data.
• Experience in another domain involving complex operational records, industry-specific taxonomies, or similarly challenging datasets.
• Experience with cloud-managed AI/ML services and modern production model lifecycle practices.
• Experience designing, managing, or governing large-scale expert labeling programs.
• Experience working with proprietary datasets as a foundation for differentiated AI products.

What Success Looks Like

Successful Lead Applied AI/ML Data Scientists at Dynatron:

• Turn difficult customer problems into AI capabilities that perform reliably in production.
• Raise the technical standard for classification, prediction, generative AI, and model evaluation.
• Help Product distinguish compelling AI opportunities from ideas that aren't technically or commercially sound.
• Build solutions appropriate to the problem rather than defaulting to the newest technology.
• Establish clear evidence that AI capabilities work before—and after—they reach customers.
• Improve models based on real-world production performance rather than treating deployment as the finish line.
• Partner effectively with Product, Engineering, Data Engineering, and MLOps from concept through production.
• Use Dynatron's proprietary automotive data to create differentiated capabilities that deliver measurable customer value.

Why Dynatron

• Help shape the AI capabilities at the center of Dynatron's next generation of products.
• Work with rich, complex automotive datasets that create opportunities for differentiated machine learning and AI.
• Influence the AI product roadmap as a senior technical authority, not simply execute predefined requirements.
• Build across traditional machine learning, generative AI, LLMs, and emerging agentic technologies.
• High-impact Lead IC role with significant technical autonomy and organizational influence.
• Partner directly with Product, Engineering, Data, and technology leadership as Dynatron continues its evolution toward an AI-first organization.
• Remote-first environment offering autonomy, ownership, and flexibility.

Compensation & Benefits

Base Salary: $180,000/yr
Benefits Include:

• Comprehensive health, dental, and vision insurance
• Equity participation through Dynatron's Equity Incentive Plan
• 401(k) with competitive company match
• Flexible vacation policy and 11 paid company holidays
• Employer-paid short- and long-term disability and life insurance
• Home office setup support
• Remote-first working environment
• Ongoing professional development opportunities

Ready to turn complex data and ambitious AI ideas into intelligent products that deliver real customer value? Join Dynatron and help define what production AI looks like across the next generation of automotive software.

Originally posted on Himalayas