Data Scientist (AI)

Location: Fully remote, but applicants must currently reside in the United States; travel in the DMV area may be expected.

Salary: $142,638–$228,221 per year

Company

hirify.global provides AI-powered decision intelligence solutions for national security, supply chain management, and digital identity.

What you will do

• Build and tune rule-weighted composite scoring logic from normalized risk signals.
• Define feature groupings, weights, thresholds, guardrails, and missing-data handling.
• Create transparent score explanations, reason codes, and traceable drivers for adjudicator review.
• Design an auditable architecture that can evolve from rule-based scoring to interpretable machine learning.
• Partner with data engineering and application teams to productionize scoring inputs, outputs, and performance requirements.
• Establish validation, quality assurance, monitoring, drift indicators, and score-distribution checks.

Requirements

• Active Top Secret security clearance must be maintained.
• Bachelor’s degree with 8–10 years of experience, master’s degree with 6–8 years, or PhD with 3–5 years; equivalent experience may substitute for a bachelor’s degree.
• 3–5 years of applied data science experience delivering scoring, ranking, or decision-support models.
• Experience with interpretable approaches, including rule-based systems, decision trees, transparent composite scores, SHAP, or permutation importance.
• Strong Python skills with scikit-learn and common data science workflows, plus hands-on SQL experience.
• Ability to explain scoring logic and accuracy-versus-interpretability tradeoffs to technical and non-technical stakeholders.

Nice to have

• Experience in adjudication, compliance, fraud, risk scoring, or related domains.
• AWS Machine Learning certification.
• Experience with Temporal Knowledge Graphs, SQL, and SPARQL.

Culture & Benefits

• Remote work arrangement with potential travel in the DMV area.
• Emphasis on transparency, traceability, analytical rigor, and defensible decision-making.
• Collaboration with data engineering and application teams.
• Documentation of scoring methodology, assumptions, limitations, and compliance artifacts.