Frederick Fox

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Staff Data ScientistAI-Native Enterprise SaaS | Risk &amp; Fraud Intelligence Platform About the RoleThis is a Staff-level individual contributor role embedded within the risk orchestration platform team, a small, high-leverage group that builds the infrastructure, tooling, and GenAI-powered workflows that other internal teams (analytics, product, customer success) depend on. What You'll Do Platform &amp; Data Infrastructure•    Architect and build scalable data pipelines and production ML workflows in collaboration with data engineering GenAI &amp; LLM Systems•    Lead the design, execution, and evaluation of Generative AI solutions Analytics &amp; Experimentation•    Develop and implement advanced analytics on top of noisy, heterogeneous data to understand user behavior, fraud patterns, and workflow effectiveness Enablement &amp; Cross-Functional Impact•    Enable product and risk teams through self-serve analytics and tools: dashboards, template analyses, and GenAI-driven assistants that help non-technical users explore data and debug What You Bring•    6+ years of hands-on experience in data science, machine learning, or high-scale data engineering, with a proven track record in fraud prevention, risk analytics, or complex decisioning systems•    Strong experience applying Generative AI in production or near-production contexts:◦    Building and evaluating LLM-based applications or agents (e.g., RAG, workflow assistants, data-insight copilots)◦    Prompt design and optimization, safety and guardrail techniques, and quantitative/qualitative evaluation of LLM outputs•    Deep proficiency in Python and SQL; hands-on experience with ML frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch) plus modern GenAI/LLM tooling •    Demonstrated experience building and maintaining scalable data pipelines and deploying ML models in production •    Platform-oriented data science experience: feature stores, model-serving infrastructure, CI/CD for ML, automated monitoring, and feedback collection workflows Preferred•    Master's or PhD in Computer Science, Machine Learning, Statistics, Engineering, or equivalent professional experience•    Direct experience with fraud/risk modeling, identity verification, or trust &amp; safety•    Prior work on orchestration platforms, case management tools, or rules/decision engines•    Familiarity with privacy-preserving ML, secure data handling, and regulatory requirements in fintech, credit, or public-sector environments•    NLP for unstructured or semi-structured data•    Experience mentoring senior ICs and setting technical direction for a small data science group

Staff Data Scientist

AI-Native Enterprise SaaS | Risk & Fraud Intelligence Platform

About the Role

This is a Staff-level individual contributor role embedded within
the risk orchestration platform team, a small, high-leverage
group that builds the infrastructure, tooling, and GenAI-powered
workflows that other internal teams (analytics, product, customer
success) depend on.

What You'll Do

Platform & Data Infrastructure

• Architect and build scalable data pipelines and production
ML workflows in collaboration with data engineering

GenAI & LLM Systems

• Lead the design, execution, and evaluation of Generative AI
solutions

Analytics & Experimentation

• Develop and implement advanced analytics on top of noisy,
heterogeneous data to understand user behavior, fraud patterns,
and workflow effectiveness

Enablement & Cross-Functional Impact

• Enable product and risk teams through self-serve analytics
and tools: dashboards, template analyses, and GenAI-driven
assistants that help non-technical users explore data and debug

What You Bring

• 6+ years of hands-on experience in data science, machine
learning, or high-scale data engineering, with a proven track
record in fraud prevention, risk analytics, or complex
decisioning systems

• Strong experience applying Generative AI in production or
near-production contexts:

◦ Building and evaluating LLM-based applications or agents
(e.g., RAG, workflow assistants, data-insight copilots)

◦ Prompt design and optimization, safety and guardrail
techniques, and quantitative/qualitative evaluation of LLM
outputs

• Deep proficiency in Python and SQL; hands-on experience with
ML frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch) plus
modern GenAI/LLM tooling

• Demonstrated experience building and maintaining scalable
data pipelines and deploying ML models in production

• Platform-oriented data science experience: feature stores,
model-serving infrastructure, CI/CD for ML, automated monitoring,
and feedback collection workflows

Preferred

• Master's or PhD in Computer Science, Machine Learning,
Statistics, Engineering, or equivalent professional experience

• Direct experience with fraud/risk modeling, identity
verification, or trust & safety

• Prior work on orchestration platforms, case management
tools, or rules/decision engines

• Familiarity with privacy-preserving ML, secure data
handling, and regulatory requirements in fintech, credit, or
public-sector environments

• NLP for unstructured or semi-structured data

• Experience mentoring senior ICs and setting technical
direction for a small data science group

Staff Data Scientist, RiskOS

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Job Description