Decision Research Scientist

We are looking for individuals who demonstrate:

• Strong grounding in decision theory, systems modeling, or applied reasoning

• Experience formalizing ambiguous problems into structured frameworks

• Comfort working with AI systems and reasoning architectures

• Ability to design and evaluate experiments rigorously

• Intellectual independence and systems thinking

Candidates may come from applied AI research, quantitative modeling, computational social science, economics, operations research, or advanced analytics domains.

Decision Research Scientist

Team: Research & Development
Location: Flexible / Remote
Reporting to: Head of R&D

Rwazi advances decision systems - software capable of structured reasoning under real-world constraints.

The Decision Research Scientist applies formal reasoning, modeling, and experimentation to real enterprise decision problems.

This role operates at the boundary between research and application.

It translates abstract decision theory, evaluation logic, and structured reasoning into practical system improvements that enhance Rwazi's decision intelligence.

This is applied decision research - not academic isolation.

Strong performance in this role results in:

• More reliable and explainable decision outputs

• Increased reasoning depth across signal types

• Expansion into new classes of enterprise problems

• Stronger structural defensibility

This role deepens Rwazi's intellectual core.

The Decision Research Scientist advances Rwazi's applied decision intelligence by turning complex reasoning into structured, validated system logic.

The Decision Research Scientist is accountable for:

• Designing formal decision frameworks for complex enterprise problems

• Modeling tradeoffs, uncertainty, and signal ambiguity

• Advancing structured reasoning methodologies

• Testing and validating new decision architectures

• Converting research insight into system-ready primitives

This role strengthens the reasoning depth of Rwazi's decision engine.

• Formalize complex business questions into structured decision systems

• Model uncertainty, tradeoffs, and multi-variable constraints

• Design evaluation logic for ambiguous signal environments

• Develop structured judgment methodologies

• Apply theoretical frameworks to real client use cases

• Test decision logic against live or simulated environments

• Identify structural weaknesses in existing decision pathways

• Propose formal improvements with measurable rigor

• Design controlled experiments to validate reasoning enhancements

• Define metrics for decision quality and output consistency

• Compare alternative system architectures under defined constraints

• Collaborate with Research Engineers to prototype research concepts

• Provide formal specifications to Product and Engineering

• Advise leadership on long-term decision capability evolution

• This is not feature delivery

• This is not surface-level analytics

• This is not academic research detached from application

This role requires rigorous thinking applied to real-world constraints.