Applied Scientist - AI / ML

About the Role

CDK Global is transforming the automotive retail experience through cutting-edge AI innovations. We are looking for an exceptional Applied Scientist to join our Applied AI Engineering team and help build intelligent, scalable, and impactful solutions for dealerships, OEMs, and consumers.

As an Applied Scientist, you will work on end-to-end development of machine learning and GenAI use-casesranging from recommendation engines and forecasting models to classification systems and generative AI applications. You will be part of a cross-functional team driving real-world business outcomes using advanced AI/ML and data science.

Why CDK?

• Be a part of redefining automotive retail using next-gen AI
• Collaborate with a diverse, global, and innovative team
• Opportunity to experiment with frontier AI techniques including Generative AI and RAG
• Competitive compensation, benefits, and a growth-oriented culture

What Youll Do

• Design, build, and deploy ML models for recommendation systems, service forecasting, classification problems, and Gen AI-powered applications
• Own the full lifecycle of AI/ML projectsfrom data exploration and feature engineering to model training, evaluation, and deployment
• Collaborate with product managers, data engineers, MLOps teams, and architects to create production-ready solutions
• Leverage LLMs (Large Language Models) and Retrieval-Augmented Generation (RAG) for intelligent customer experiences and automation
• Ensure high model performance, interpretability, and scalability across diverse datasets and use-cases
• Contribute to building reusable AI/ML components and pipelines to accelerate development velocity

Basic Qualifications

• Masters degree or PhD in Computer Science, Machine Learning, Data Science, Statistics, or a related technical field
• 3+ years of experience in building machine learning models for real-world business applications
• Strong programming skills in Python
• Experience with key ML techniques including regression, classification, collaborative filtering, and deep learning
• Familiarity with model deployment tools (e.g., FastAPI, Docker, AWS/GCP/Azure ML services)
• Solid understanding of data structures, algorithms, and software engineering principles

Preferred Qualifications

• Proven experience in recommendation engines, time-series forecasting, and NLP/GenAI (e.g., OpenAI, LangChain, Transformers)
• Experience with deploying models in production environments and maintaining MLOps workflows
• Strong problem-solving skills with the ability to translate business objectives into technical solutions
• Demonstrated thought leadership via patents, publications, or internal innovations
• Comfortable working in fast-paced, ambiguous environments where autonomy and self-direction are key