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The Role
Lead modernization and scaling of an anomaly-detection Decision Intelligence Platform: redesign rule and anomaly engines, develop supervised and unsupervised models, ensure full explainability and audit-ready validation, implement drift monitoring and versioning, produce documentation and test artifacts for independent reruns, and quantify oversight impact for state auditors.
Summary Generated by Built In
We are a growing information technology company that offers its employees a culture of success, the chance to work on revolutionary federal IT infrastructure, and the opportunity to grow alongside cutting-edge technology that is reshaping the industry. We are seeking forward thinking candidates that have strong experience in operational support and can help take to the next level in a pro-active stance.
Chameleon Integrated Services has expertise in operations management, quality systems, data operations and cybersecurity. We secure some of the most sensitive data for the Department of Defense and for other U. S. federal government agencies. We are known for the great care we take with clients and employees, and we believe in promoting from within.
Senior Data Scientist / AI-ML & Anomaly Detection Lead
Position Overview
- Position Type: Part-Time Consultant / Technical Subject Matter Expert
- Target Allocation: 14–18 hours/week average (Note: Workload is highly concentrated around phase deliverables involving rule modernization, anomaly analytics engine build-out, technical validation, and state acceptance testing).
- Location: Remote (U.S. Based) with periodic travel to Tallahassee, FL as required.
This platform will unify statewide oversight, tracking abnormal spending patterns, contract vulnerabilities, and fraud/waste/abuse risks across up to 35 state agencies. Because this is a high-visibility, firm-fixed-price (FFP) state government contract, you will maintain absolute accountability for achieving legally binding quantitative thresholds, including a 95% or greater rule-output accuracy rate and a 5% or lower false-positive rate.
Principal Responsibilities
- POC Library Modernization: Evaluate, enhance, and modernize the existing 11-rule baseline POC library to support full enterprise scalability.
- Anomaly Engine Design: Architect transaction-centric anomaly logic, defining data features, risk scoring models, tolerance thresholds, and alert prioritization criteria.
- Machine Learning Optimization: Develop, refine, and deploy supervised and unsupervised machine learning algorithms where they add measurable validation value over standard deterministic rules.
- Model Explainability & Traceability: Maintain absolute, non-black-box transparency across all algorithms. Ensure every flagged transaction generates clear, human-readable logic explanations and evidence usable by state auditors or Inspector General investigators.
- Rigorous Validation Testing: Establish comprehensive, audit-ready validation datasets to measure, verify, and document model accuracy, false-positive metrics, and rule reproducibility.
- Independent Rerun Support: Provide complete technical documentation, test scripts, and system logs to enable independent OCIG technical validation teams to successfully rerun all anomaly detection routines.
- Drift & Performance Monitoring: Develop and implement automated pipeline criteria for model scoring transparency, versioning control, feature mapping, and data drift detection.
- Value-Realization Analytics: Engineer standardized formulas and methodologies to compute quantifiable oversight impacts, including potential cost avoidance, financial recoveries, identified risk exposure, and investigative referrals.
Required Qualifications
- Experience Baseline: 10+ years of comprehensive data analytics and data science experience, with 5+ years of dedicated, hands-on machine learning engineering.
- Government Context: Documented history delivering data science, predictive modeling, or advanced analytics solutions within a federal, state, military, or local government framework.
- Core Tech Stack: Advanced, hands-on mastery of Python and SQL for complex data manipulation and engineering.
- Advanced Analytics Toolkit: Deep expertise across supervised and unsupervised learning, classification, clustering, statistical forecasting, and advanced feature engineering.
- Model Calibration: Proven experience in model evaluation, threshold calibration, and exhaustive false-positive or false-negative impact analysis.
- Investigative Translation: Demonstrated ability to translate raw model outputs into defensible, audit-ready forensic evidence rather than merely outputting an unweighted probability score.
- Scientific Reproducibility: Experience producing comprehensive technical artifacts, configuration baselines, and model documentation sufficient for independent third-party replication.
- Domain Expertise: Strong experience working with highly disparate, transaction-level data sets including financial, procurement, contract management, or purchasing card ledger systems.
- Vetting & Location: Must be a U.S.-based citizen or resident. Must be able to successfully clear an FDLE Level II background screening (including fingerprinting) within 5 business days of contract award.
Strong Preferences
- Prior experience engineering fraud, waste, abuse, improper payment, financial crime, or corporate risk analytics models.
- Working context with data from oversight agencies such as Treasury, FinCEN, CMS, Census, or state-level Medicaid and revenue departments.
- Hands-on production experience utilizing Azure Databricks, MLflow, or Azure Machine Learning within secure Government Cloud environments.
- Deep familiarity with building hybrid detection architectures that seamlessly blend deterministic business rule repositories with machine learning anomaly detection.
- Prior usage of formal model cards, explainability packages, and structured human-in-the-loop validation review workflows.
MANDATORY RESUME FORMATTING INSTRUCTIONS
The State of Florida strictly evaluates and verifies all named staff experience for this contract. Generic resumes that only list generalized technical summaries or state they are "familiar with AI/ML" will be automatically rejected.
To be considered for this role, your resume must explicitly detail actual project case studies for your past contract positions, utilizing the following structure:
- The Government Customer: Explicitly name the agency and the specific system context (e.g., Treasury, DHS, DLA).
- The Specific Problem: Detail the exact compliance, oversight, or fraud variant the model was designed to detect.
- The Technical Approach: Name the specific datasets, algorithms, models, and toolsets utilized (e.g., Clustering, SAS Enterprise Miner, Python).
- Personal Contribution: Detail exactly what you personally engineered, configured, or deployed regarding features, thresholds, or pipelines.
- Deployment & Validation Status: State the true production or operational status of the project, including the exact validation method used to prove model accuracy.
- Quantifiable Outcomes: Provide the measurable results achieved, such as false-positive reduction rates, percentage of pipeline reliability, or volume of cost avoidance unlocked.
Skills Required
- 10+ years of comprehensive data analytics and data science experience
- 5+ years of hands-on machine learning engineering experience
- Documented delivery of data science or predictive modeling solutions in federal, state, military, or local government contexts
- Advanced, hands-on mastery of Python for complex data manipulation and engineering
- Advanced, hands-on mastery of SQL for complex data manipulation and engineering
- Deep expertise in supervised and unsupervised learning, classification, clustering, forecasting, and advanced feature engineering
- Proven experience in model evaluation, threshold calibration, and false-positive/false-negative impact analysis
- Ability to translate model outputs into audit-ready forensic evidence and human-readable explanations
- Experience producing comprehensive reproducible technical artifacts, configuration baselines, and model documentation for independent replication
- Strong experience with disparate transaction-level datasets (financial, procurement, contract management, purchasing card ledgers)
- Accountability for achieving legally binding quantitative thresholds (>=95% rule-output accuracy, <=5% false-positive rate)
- Must be U.S.-based citizen or resident and able to clear an FDLE Level II background screening (including fingerprinting) within 5 business days of contract award
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The Company
What We Do
Chameleon Integrated Services is an information technology company offering requirements management services.
