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About This Role
Clearance Level
None
Category
Data Science and Data Engineering
Location
Remote, Working from the USA
Key Skills For Success
Amazon Web Services (AWS)
Healthcare Claims
Predictive Modeling
Python (Programming Language)
Supervised Learning
##### REQ\#:RQ225761
##### Public Trust:None
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to be on the frontlines of health innovation. Deliver for America’s health agency missions and enhance lives for hundreds of millions every day.
Job Description
-------------------
As the Senior Data Scientist for Machine Learning supporting the Healthcare Fraud Prevention Partnership (HFPP), you will be the first dedicated machine learning practitioner at the Trusted Third Party (TTP), an established Fraud, Waste and Abuse (FWA) analytics program. You will develop predictive models against a multi\-billion record claims warehouse assembled from dozens of public and private healthcare payers, and you will establish how machine learning models move from development into production on this program.
The data, the subject matter experts and the payer partnerships are already in place; the modeling capability is yours to build. This is a senior individual contributor position without direct reports, and it is the only role on the team focused primarily on machine learning, meaning the Senior Data Scientist will be establishing practice rather than joining one.
\*\*\*Work visa sponsorship will not be provided for this position. This is a remote role. Candidates must reside in the United States.
MEANINGFUL WORK AND PERSONAL IMPACT:
- Designing, training and validating supervised models that score providers and billing patterns for FWA risk, using investigative case\-level data, payer feedback on referred leads, and public exclusion and enforcement data as labels, including the entity resolution to link enforcement records to providers in claims.
- Designing validation for the actual conditions: labels lagging billing behavior by years, coverage limited to leads previously referred, extreme class imbalance, and schemes that shift faster than confirmation arrives.
- Engineering features against billions of claim records within the warehouse rather than extracting data to local memory, using Python and SQL, alongside data engineers and Business Intelligence Developers.
- Delivering output that supports action. Investigators need the specific claims, the pattern and the basis for the finding, so each model carries a human\-readable rationale and claim\-level evidence alongside the score, adjusted for case mix and specialty and ranked so that precision at the top of the review queue is the operative measure.
- Deploying models into production and keeping them healthy, including scheduled execution, versioning and drift monitoring, and establishing the modeling and deployment practices the Data Science team adopts going forward.
- Collaborating with FWA Subject Matter Experts to separate genuine anomalies from patterns explained by coverage policy or claim edits, and communicating methodology and limitations to HFPP Partners and stakeholders so that output is adopted and acted upon.
WHAT YOU'LL NEED TO SUCCEED:
- Master's in a quantitative field (statistics, computer science, engineering, applied mathematics or related), or a Bachelor's with equivalent hands\-on experience.
- 5\+ years building, validating and delivering supervised machine learning models on real\-world data, including work in which labels were incomplete, delayed or biased.
- Experience deploying models into production and maintaining them: scheduling execution, versioning and drift monitoring, with data engineers.
- Python and SQL, including feature engineering within the data warehouse at very large scale rather than extracting to a local environment.
- 2\+ years working with healthcare claims data (Medicare, Medicaid or commercial) and coding systems (e.g., ICD\-10, CPT, HCPCS, DRG).
- Experience with validation design for imbalanced, temporally shifting problems: out\-of\-time evaluation, leakage detection, calibration and precision\-focused metrics over ranked output.
- Ability to explain model output to a non\-technical investigator, defend methodology to technical audiences, and present analytic outcomes to clients and stakeholders.
DESIRED QUALIFICATIONS:
- Graph or network analytics, entity resolution and record linkage for identifying collusive relationships across payers.
- Positive\-unlabeled, semi\-supervised or active learning against a capacity\-constrained review queue.
- Modeling in a regulated or adverse\-action setting where explainability and fairness were requirements.
- Anomaly detection, peer\-group construction and case\-mix methods (e.g., HCC); AWS and/or Snowflake, including Snowpark or model lifecycle tooling.
- Healthcare FWA or program integrity datamining in multi\-payer databases; payer coverage policy (LCDs, NCDs) and claim edits (e.g., NCCI).
SECURITY CLEARANCE LEVEL:
- Must be able to obtain/maintain Public Trust.
GDIT IS YOUR PLACE:
The GDIT HFPP TTP is the only data warehouse of its type anywhere, bringing many billions of claims from dozens of public and private payers together solely for fraud, waste and abuse analytics. The cross\-payer visibility it provides exists nowhere else.
A combination uncommon in machine learning roles: mature data and established subject matter expertise in place, with the modeling and production practices yours to define.
OWN YOUR OPPORTUNITY:
Explore a career in data science and engineering at GDIT and you'll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges.
### Work Requirements
Years of Experience
5 \+ years of related experience
- may vary based on technical training, certification(s), *or* degree
Certification
Travel Required
Less than 10%
### Salary and Benefit Information
The likely salary range for this position is $123,250 \- $166,750\. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
### Our Identity Verification Process
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.
### About Our Work
We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50\+ countries worldwide, offering leading mission\-ready capabilities in AI, cloud, cyber and software development.
Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.
*Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans*
Salary Context
This $123K-$166K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At General Dynamics Information Technology, 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 25% below the category median. Disclosed range: $123K to $166K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 13 open AI roles right now. They're hiring across Data Scientist, Data Engineer, AI/ML Engineer, AI Software Engineer. Positions span Remote, US, Arlington, VA, US, Chantilly, VA, US. Compensation range: $154K - $287K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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