Yum Crave AI is seeking a Senior Manager, Data Science & AI Engineering to lead and scale our hybrid team of Data Scientists and AI Engineers across traditional ML and next-generation AI systems. This individual will oversee workstreams that span forecasting, personalization, experimentation, and intelligent agent development using both structured data and foundation models (LLMs, VLMs, etc.)

The ideal candidate is an experienced technical leader who thrives at the intersection of applied data science and LLM-driven development, and who can coach teams, collaborate cross-functionally, and communicate complex ideas clearly to technical and executive audiences.

• 8+ years of experience in Data Science, Machine Learning, or AI Engineering.
• Experience leading and coaching high-performing technical teams.
• Hands-on expertise in both traditional ML (regression, classification, forecasting) and modern AI techniques (LLMs, RAG, embeddings, multimodal models).
• Strong communication skills with the ability to influence senior leadership and simplify complex topics.

Preferred Qualifications:

• Experience building or scaling hybrid DS+AI teams in enterprise environments.
• Familiarity with Lang Chain, Hugging Face, OpenAI, Pinecone, or vector-based ML architectures.
• Exposure to business domains such as QSR, retail, personalization, or intelligent automation.

Salary Range: $165,700 - $226,800 annually + bonus eligibility. This is the expected salary range for this position. Ultimately, in determining pay, we'll consider the successful candidate’s location, experience, and other job-related factors.

Team Leadership & Coaching:

• Lead and mentor a team of Data Scientists and AI Engineers across multiple domains and products.
• Provide technical and career development guidance; foster a collaborative, high-trust culture.
• Drive standards for experimentation, evaluation, reproducibility, and model performance.
• Technical Strategy & Execution: Shape end-to-end ML and AI strategy across core DS use cases (forecasting, pricing, recommendation) and emerging LLM/VLM applications.
• Guide architectural choices around model selection, evaluation design, and deployment workflows.
• Provide oversight on LLM fine-tuning, prompt design, ASR/embedding adaptation, and production readiness.

Communication & Collaboration:

• Act as a key interface between technical contributors and business/product stakeholders.
• Distill analytical output and AI system behavior into clear presentations, dashboards, or executive summaries.
• Partner with Product, Engineering, TPM, and Ops to align team output with company priorities.