Decision Research Scientist
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 199d ago
The date the source published, not the day we noticed it (2026-02-27). Last seen at its source 1h ago.
Is it remote?
Global (Remote)
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Available worldwide
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $108.9k–158.8k/yr
Middle 50% of 11 listings that do state pay — Operations · all levels · Worldwide · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
ashby employer's own board first seen 11d ago · last seen 1h ago
The listing
Team: Research & Development
Location: Flexible / Remote
Reporting to: Head of R&D
Role Overview
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.
Core Mandate
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.
Key Responsibilities
Applied Decision Modeling
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
Research-to-Application Translation
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
Experimental Design
Design controlled experiments to validate reasoning enhancements
Define metrics for decision quality and output consistency
Compare alternative system architectures under defined constraints
Cross-Functional Integration
Collaborate with Research Engineers to prototype research concepts
Provide formal specifications to Product and Engineering
Advise leadership on long-term decision capability evolution
Role Impact
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.
What This Role Is Not
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.
Qualifications and Profile
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.
How Candidates Are Evaluated
Candidates are evaluated based on:
Their ability to formalize complex decision problems
The rigor and clarity of their reasoning models
Evidence of applying theory to practical systems
Depth of structured thinking
Their ability to improve decision quality measurably
Summary
The Decision Research Scientist advances Rwazi’s applied decision intelligence by turning complex reasoning into structured, validated system logic.