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About This Role
This position supports a potential Department of the Air Force opportunity and is contingent upon contract award, final Government requirements, and candidate qualification and clearance verification. This posting does not represent an offer of employment and does not imply that a contract has been awarded or that any offeror has been selected.
IDS is building the technical leadership for a federal applied research program in acquisition cost analytics. The Chief Cost Data Scientist is the contractor senior technical authority for the entire effort: enterprise architect for the customer cloud cost data environment, technical lead for quality and consistency across every task and deliverable, and primary strategic advisor to the customer technical director on data strategy, technology insertion, and analytical innovation.
This is not a conventional data science management role. The position requires genuine depth on both sides of a rare combination: defense cost estimating and applied machine learning research. Candidates who have one without the other will not meet the threshold.
Location: Remote or hybrid, United States. Occasional travel to the National Capital Region.
Clearance Type: Active Secret required at hire. Top Secret with SCI eligibility strongly preferred.
Essential responsibilities
- Serve as senior technical authority across all tasks, including technical oversight of all pipeline designs, analytical methods, deployed products, and subcontractor technical work.
- Serve as enterprise architect for the customer cloud cost data environment: own architecture decisions, technology stack recommendations, and platform evolution, and approve contractor-level decisions within a tiered government decision framework.
- Own experimental design, statistical governance, model selection policy, validation criteria, and reproducibility standards.
- Make machine learning applicability determinations: decide where ML earns its place over transparent parametric methods, and document the sample size and data quality conditions under which each holds.
- Identify and present technology insertion recommendations to the customer, stating the expected benefit, impact on existing pipelines and products, level of effort, and risk.
- Serve as primary strategic advisor to the customer technical director, and work with the program manager to identify adoption barriers and integrate products into cost estimating workflows.
- Ensure technical content quality, commonality, and configuration compliance across all tasks, and author or approve the technical research reports.
- Drive research and development initiatives in machine learning and artificial intelligence, and translate complex analytical results into actionable recommendations for decision makers.
Required qualifications
- Bachelor of Science or Bachelor of Arts from an accredited institution. Advanced degree in a quantitative discipline strongly preferred.
- Minimum ten years as a data scientist.
- Minimum five years in cost estimating or cost analysis.
- Minimum five years performing projects of comparable scope.
- Leadership in data science: experience leading cross-functional teams of data scientists, data engineers, and data or cost analysts, and defining a data strategy aligned to organizational goals.
- Demonstrated expertise integrating advanced data science capability with existing processes and technologies.
- Demonstrated history of driving research and development initiatives in machine learning and artificial intelligence.
- Knowledge and experience performing cost estimating and analysis on major defense systems, including typical work breakdown structures and standard cost data such as cost and software data reporting, operating and support data, earned value data, selected acquisition reports, and budget data.
- Demonstrated experience developing cost estimating relationships and performing analogy estimating.
- Exceptional communication skills, including translating complex analytical results into actionable recommendations for senior stakeholders.
- Active Secret clearance. United States citizenship required.
Preferred qualifications
- Doctorate in statistics, operations research, economics, engineering, or a related quantitative field.
- Published research record in cost estimating, applied statistics, or machine learning.
- Experience with Databricks, Apache Spark, Python, R, and SQL in a governed cloud environment.
- Prior support to a service cost agency, the Office of the Secretary of Defense, or a major defense acquisition program office.
- Top Secret clearance with SCI eligibility.
This position is expected to pay $185,000 to $225,000 annually; depending on experience, education, and any certifications that are directly related to the position
IDS is committed to providing superior work in the fields of science, engineering, data analytics and technology to government agencies. We offer competitive compensation packages, including comprehensive nationwide Medical/Dental/Vision insurance programs, life insurance, matching 401k contribution and Educational/Training support.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $185K-$225K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At INNOVIM DEFENSE SERVICES, LLC, 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 1,406 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($205K) sits 8% above the category median. Disclosed range: $185K to $225K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
INNOVIM DEFENSE SERVICES, LLC AI Hiring
INNOVIM DEFENSE SERVICES, LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $225K - $225K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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