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About This Role
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
Knowesis, Inc. is seeking a highly experienced Technical Analytics Manager/Lead Data Scientist to lead a multidisciplinary team providing end-to-end analytic and investigative support to the Pandemic Response Accountability Committee (PRAC). The PRAC delivers data-driven oversight of pandemic relief and OBBBA Act funds, supporting fraud prevention and detection efforts across federal programs. As an independent oversight committee within the Council of the Inspectors General on Integrity and Efficiency (CIGIE), the PRAC works closely with federal Offices of Inspectors General (OIGs) and federal law enforcement to ensure pandemic-related funding is used appropriately and not subject to fraud, waste, or abuse.
The Technical Analytics Manager/Lead Data Scientist will support the PRAC Analytics Center in conducting data analysis and visualization; sharing leading data analytics practices; providing tools and services; and providing investigative intelligence. The PRAC analytic platform develops analytic tools and products in its cloud-based environment. The PRAC currently uses Azure Databricks, Microsoft SQL Server, Microsoft Power BI, neo4j and other tools and technology to analyze and visualize public, non-public and commercially available data and to provide insights. The PRAC analytics platform operates through a series of agile sprints working with other PRAC internal stakeholder teams via Integrated Project Teams (IPT), and in a manner that uses business and service delivery models that will optimize its ability to scale up or down based on OIG community user demands.
The 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 approaches to detect and prevent fraud, waste, abuse, and mismanagement.
- Five (5) or more years of experience tracking project progress, identifying risks and issues, and implementing mitigation strategies to ensure timely, high-quality delivery of analytic projects.
- Five (5) or more years of experience conducting detailed review and quality control of contractor or team work products prior to finalizing analytic models, outputs, and insights.
- Five (5) or more years of hands-on coding experience developing analytic rules, models, and pipelines using open-source programming languages and tools (e.g., Python, R, SQL, Spark, etc.).
- Strong oral and written communication skills, including the ability to explain complex analytic concepts to senior leaders, investigators, and non-technical stakeholders.
Preferred Qualifications:
- Experience supporting fraud analytics, program integrity, or investigative support within federal oversight environments (e.g., OIGs, law enforcement, PRAC, CIGIE).
- Experience integrating multi-agency or multiprogram datasets to detect complex, emerging fraud schemes in high-risk or time-sensitive environments.
- Experience with graph analytics platforms (e.g., neo4j) and link analysis techniques for identifying hidden relationships and cross-program risks.
- Experience developing or deploying AI/ML models in compliance with federal AI governance frameworks (OMB M-25-22, M-25-21, M-26-04; NIST AI RMF).
- Experience leading or mentoring multidisciplinary analytic teams (data scientists, statisticians, engineers).
- Experience supporting enterprise data management (EDM), data governance, or federal data quality standards.
- Advanced certifications in data science, fraud examination, or analytics (e.g., CFE, Certified Data Scientist, AWS/Azure ML certifications).
Required Education: Bachelor’s degree (BA/BS) in Data Science, Computer Science, Statistics, Mathematics, Engineering, Information Systems, or a related quantitative field. Advanced degree preferred.
Benefits:
- Health (PPO & HDHP) Insurance, Dental, Vision, STD & LTD, Basic Life Insurance, 401k Company Match, & Voluntary Products.
Knowesis is committed to providing equal employment opportunities to all individuals based on merit and qualifications. We prohibit discrimination in all aspects of employment as required by Title VII of the Civil Rights Act and other applicable federal laws. Our company values all applicants and employees and fosters a work environment where everyone is treated with respect and dignity.
Salary Context
This $155K-$200K range is above the median for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Knowesis Inc., this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($177K) sits 7% below the category median. Disclosed range: $155K to $200K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Knowesis Inc. AI Hiring
Knowesis Inc. has 2 open AI roles right now. They're hiring across Data Scientist. Based in Washington, DC, US. Compensation range: $165K - $200K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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