Technical Analytics Manager/Lead Data Scientist

Knowesis Inc.; Washington (District of Columbia); Remote; USD 155,000 - 200,000; Full time

Job summary

Knowesis Inc. seeks a Technical Analytics Manager/Lead Data Scientist to lead a multidisciplinary team of data scientists, analysts, statisticians, and data engineers in fraud analytics and investigative data science.

The role emphasizes analytic integrity, reproducibility, and clear communication with senior leaders and law enforcement partners. The position is remote with occasional site visits, requires Public Trust clearance, and offers a salary range of $155,000–$200,000 per year,

Qualifications

• 5+ years in analytic rules and models for fraud detection.
• 5+ years in designing analytic approaches and managing model development lifecycles.
• Experience ideating and shaping analytic use cases with innovative approaches.

Responsibilities

• Lead a multidisciplinary team of data scientists, analysts, statisticians, and data engineers.
• Ensure analytic integrity, reproducibility, and compliance across technical workstreams.
• Present findings and investigative insights to senior leadership and stakeholders.

Skills

Fraud analytics

Data science leadership

Stakeholder communication

Tools

Neo4j

Open-source tools

Job description

Brief Description

Position: Technical Analytics Manager/Lead Data Scientist

Location: Remote, with occasional site visits to PRAC locations as required

Work Environment: Remote

Clearance Required: Public Trust (HSPD-12)

Status: Contingent Upon Contract Award

Salary Range: $155,000 - $200,000 per year

Job Duties and Responsibilities include but are not limited to the following:

• Lead and mentor a multidisciplinary team of data scientists, analysts, statisticians, and data engineers.
• Ensure analytic integrity, reproducibility, and compliance across all technical workstreams.
• Communicate complex analytic concepts to senior PRAC leadership, OIG partners, and non-technical stakeholders in clear, actionable terms.
• Oversee risk management, quality assurance, and performance monitoring for all analytic and investigative support activities.
• Foster innovation, continuous improvement, and adoption of best practices in fraud analytics and investigative data science.
• Lead analytic support for investigative case development using open-source, law enforcement, and third-party data sources.
• Produce customized investigative work products such as intelligence reports, link analysis diagrams, entity relationship charts, and fraud scheme visualizations.
• Apply graph analytics (including neo4j) to analyze complex networks and visualize relationships across multi-agency datasets.
• Conduct due diligence and verification of PRAC-developed analytic rules and model outputs to support investigative lead generation.
• Collaborate with OIGs and federal law enforcement partners following CIGIE/PRAC policies for handling law-enforcement-sensitive data.
• Present findings, investigative leads, and analytic insights to OIG and law enforcement stakeholders through briefings, demonstrations, and technical walkthroughs.
• Ensure investigative support workflows, templates, and documentation meet PRAC standards for accuracy, clarity, and evidentiary integrity.
• Conduct market research on emerging analytic tools, AI/ML technologies, data sources, and fraud detection capabilities; provide recommendations aligned with PRAC mission needs and federal policy.
• Collaborate with PRAC's OCIO, OCISO, and OCDO to evaluate, configure, and integrate approved tools into the PRAC analytic environment.
• Develop iterative implementation plans for new capabilities, ensuring smooth integration, configuration, and operational readiness.
• Support ingestion of new data sources, including schema design, transformation logic, and quality control processes.
• Assist PRAC in evaluating cutting-edge AI and IT tools, providing technical analysis, documentation, and risk considerations to support Government procurement decisions.
• Maintain awareness of emerging fraud schemes, analytic technologies, and cross-program risks to inform future capability development.

Required Qualifications

• Five (5) or more years of hands-on experience developing analytic rules and models for fraud detection use cases using leading-edge analytic tools, open-source programming techniques, and industry best practices.
• Five (5) or more years of experience designing analytic approaches, managing model development lifecycles, conducting model testing, and performing thorough quality control and validation.
• Demonstrated experience ideating and shaping analytic use cases using innovative approa