AI/ML & Advanced Analytics Lead

Role Summary

• We are looking for a senior AI/ML and Advanced Analytics professional to lead design, development, validation, implementation, and monitoring of machine-learning and predictive-analytics capabilities supporting SNAP eligibility determination.
• The individual should have strong experience applying ML/predictive analytics in regulated operational environments, preferably government benefits, healthcare, Medicaid, SNAP, financial assistance, fraud detection, eligibility, or case management.
• This is not a generic Data Scientist role. The candidate should have experience deploying production decision-support models within operational workflows.

Key Responsibilities

The candidate should be capable of leading:

• Assessment of available data sources, data quality, completeness, integrity, usability, and limitations.
• Exploratory and statistical analysis of eligibility-related data.
• Identification, categorization, quantification, and prioritization of potential eligibility determination errors.

Identification of:

• Risk indicators
• Error patterns
• Trends
• Correlations
• Anomalies
• Predictive factors
• Development of predictive analytics and machine-learning models.
• Development of case-level risk scoring and prioritization methodologies.
• Generation of eligibility decision-support recommendations.
• Development of confidence scoring mechanisms.
• Development of explainable AI capabilities identifying:
• Primary recommendation factors
• Policy considerations
• Data elements evaluated
• Model confidence
• Model version
• Limitations
• Model validation and performance evaluation.
• Monitoring of:
• Accuracy
• Precision
• Recall
• False positives
• False negatives
• Model drift
• Demographic impact
• Recommendation acceptance rates
• Override rates
• Documentation of:
• Training data
• Feature-selection methodology
• Data preparation
• Validation datasets
• Model assumptions
• Model limitations
• Model changes
• Fairness/disparate-impact analysis as applicable.
• Root-cause analytics for recurring eligibility errors.
• Collaboration with business SMEs, architects, developers, and DCF stakeholders.
• Support for UAT, model acceptance, production-readiness assessment, and post-deployment performance monitoring.
• The SOW specifically requires explainability and continuous monitoring across accuracy, precision, recall, false-positive/negative rates, drift, demographic impact, acceptance rates, performance and limitations.

Mandatory Experience

• 8 12+ years analytics/data-science experience, including 3+ years implementing production ML/predictive solutions.

Candidate should demonstrate:

• Advanced analytics and machine-learning experience.
• Production implementation of predictive models.
• Decision-support recommendation systems.
• Risk-scoring or classification models.
• Model validation and performance monitoring.
• Explainable AI/model interpretability.
• Experience with structured government/healthcare/financial/case data.
• Experience in at least one regulated environment such as:
• Government
• Healthcare
• Medicaid
• SNAP/public benefits
• Financial services
• Fraud detection
• Case management

Eligibility

• Experience deploying analytics within operational workflows rather than only research/POC environments.
• This profile is particularly important for Section 5.4.4, which requires demonstrated experience implementing ML or predictive analytics solutions that provide decision-support recommendations within operational workflows in eligibility, government, healthcare, financial assistance, case-management, or comparable regulated environments.

Technical Skills

Strong experience with combinations of:

• Python, SQL, Scikit-learn, XGBoost/LightGBM, TensorFlow/PyTorch where applicable, predictive modeling, classification, anomaly detection, feature engineering, model validation, model explainability, SHAP/LIME or equivalent approaches, ML pipelines, model governance, drift monitoring, data-quality assessment and statistical analysis.
• Cloud/platform experience can include AWS, Azure, GCP, Databricks, Snowflake or comparable enterprise platforms.

Preferred Domain Experience

Priority candidates will have experience with:

SNAP > Medicaid/Public Benefits > Integrated Eligibility > HHS > Fraud/Program Integrity > Healthcare/Financial Assistance.