Applied AI / ML & Quantitative Solutions Engineer
Role Overview:
Senior-level role combining applied ML, statistics, data engineering, and modern AI tools to solve real business problems. This is not an LLM-research role; the focus is on practical modeling, production deployment, and independent problem-solving with ambiguous requirements.
Key Responsibilities:
• Translate business problems into statistical, analytical, or ML solutions
• Select, build, evaluate, and deploy models beyond notebooks into production
• Independently query and investigate data using SQL in a Snowflake environment
• Use AI tools effectively for development and analysis; guide, validate, and redirect AI output when needed
• Apply engineering, architecture, and data-modeling judgment across ML and data solutions
• Work independently with loosely defined requirements, clarify needs, and communicate blockers and next steps
Required Skills:
• Strong mathematics, statistics, and ML model-selection experience
• Real-world experience productionizing models
• Advanced SQL and Snowflake experience
• Practical Python for applied modeling and analytics
• Hands-on use of AI tools: prompt design, AI-assisted development, agent skills, guardrails, and steering AI output
• Strong data modeling and engineering/architecture understanding
Preferred:
• Finance or Operations Research background
• Experience solving quantitative business problems
• Broader engineering and data architecture experience
Communication:
Must communicate clearly with technical and non-technical stakeholders, write clean documentation/tickets, ask strong clarifying questions, and proactively manage work, blockers, and next steps.