Applied AI / ML & Quantitative Solutions Engineer
Location: 100% Remote
Duration: 6 months + likely extension
Key Responsibilities
Applied Modeling & Quantitative Problem Solving
Translate business problems into appropriate analytical, statistical, or machine-learning approaches.
Evaluate different modeling options and determine when and why a particular model should be used, and when it should not be used.
Apply strong mathematical and statistical reasoning when selecting and evaluating models.
Build practical models intended to solve real business problems.
Take models beyond notebooks or experiments and into real production use.
Explain model-selection decisions, assumptions and tradeoffs clearly.
Data Investigation & Analysis
Independently query databases to investigate questions and obtain the data required to solve problems.
Use SQL effectively without requiring someone else to retrieve or prepare all required data.
Work with data and data models as part of the broader modeling and engineering process.
Work effectively within a Snowflake-based environment.
AI-Augmented Engineering & Problem Solving
Use modern AI tools to accelerate analytical and engineering work.
Apply appropriate instructions/prompts and understand concepts such as AI/agent skills and guardrails.
Recognize when an AI-assisted solution is becoming stuck, producing an inappropriate direction, or otherwise failing to accomplish the intended objective.
Apply independent technical judgment to redirect the approach toward the outcome being sought.
Understand enough about the underlying engineering, architecture, data, and modeling problem to guide AI rather than simply accepting its output.
Engineering & Architecture
Bring engineering and architecture thinking to analytical and AI-enabled solutions.
Understand the data model supporting the application or solution.
Work across the boundaries of data, modeling and AI rather than treating ML as an isolated notebook activity.
Be comfortable solving problems even when the required technology or implementation technique is not already familiar. During the call, Jonathan specifically emphasized wanting someone who isn't afraid to tackle something they don't already know how to do while remaining honest about genuine gaps in their expertise.
Operating with Ambiguity
Work effectively from loosely defined requirements.
Ask strong clarifying questions to uncover what business users actually need.
Determine what information is missing and independently locate the appropriate people to obtain it.
Proactively communicate blockers and pursue resolution rather than waiting for detailed direction.
Communicate when work has been completed and proactively identify the appropriate next step or additional work.
Required Qualifications
Quantitative / Modeling
Strong mathematics and statistics foundation.
Demonstrated understanding of statistical and/or machine-learning model selection.
Ability to explain why a particular model is appropriate for a problem and when another approach would be better.
Demonstrated experience building models for real-world problems.
Real-world experience taking models into production beyond notebooks or academic projects.
Data
Strong SQL skills.
Ability to independently query databases and investigate data.
Experience working with Snowflake.
Strong understanding of data and data-modeling concepts.
Programming
Practical Python capability for applied modeling and analytical development.
Applied AI
Candidates should have experience using modern AI as part of their actual technical workflow and be able to intelligently discuss areas such as:
Prompt/instruction design
AI-assisted development
Agent skills
AI guardrails
Identifying when AI is stuck or taking an inappropriate direction
Steering AI toward the intended solution