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About This Role
Data Scientist (Palantir Foundry)
Remote
At IPT Associates (IPTA), we enjoy solving real-world problems with technology. We work closely with our customers, teammates, experts, and partners to come up with practical solutions that make a difference. Whether it's a government or business organization, we focus on understanding the need and building something that works.
Our Team
At IPTA, everything starts with our people. We're big believers that when you invest in your team, great things happen. That's why we encourage learning, growth, and trying new things-even if you don't have all the answers yet.
Our culture is rooted in fierce determination, fearless integrity, and passionate service-but we also like to keep things collaborative, supportive, and down-to-earth.
We're looking for people who:
- Are curious and excited about technology
- Like solving problems and figuring things out
- Can take initiative but also enjoy working with a team
- Want to keep learning, growing, and challenging themselves
If you're someone who's eager to jump in, learn something new, and make an impact-you'll fit right in here.
Responsibilities:
- Serves as the senior data scientist on the Palantir Foundry (Army Vantage) team, turning migrated U.S. Army enterprise data into production-grade analytical products and decision-support workflows
- Understands the customer's most critical objectives and synthesizes disparate information into a cohesive vision and actionable roadmap, interpreting what the data signifies and outlining an impact-based plan from use case to prototype to scaled deployment
- Develops, validates, and operationalizes machine learning and statistical models in Foundry that help the customer make better decisions
- Designs data models, ontologies, and data structures that support analytics, reporting, and AI/ML use cases, and sets standards for data quality, integrity, and security
- Builds and maintains production-grade Foundry pipelines and transforms on structured and unstructured datasets that feed models and analytical workflows
- Provides data context during ontology design and implements the pipelines backing ontology objects and links
- Collaborates with engineers, program managers, and domain specialists to define solution requirements, iterate on solutions, and ship code
Requirements:
- Bachelor's degree in Computer Science, Data Science, Statistics, or a related field
- 10+ years of experience in data science, advanced analytics, or data architecture, including proven hands-on work in Palantir Foundry
- Advanced data science expertise, including statistical analysis, machine learning, and production model development in Python
- Advanced Palantir Foundry experience building data pipelines, managing datasets, developing ontologies, and deploying applications
- Demonstrated ability to build production-grade code that leverages both structured and unstructured datasets
- Willingness to travel as needed for client implementation work
- Design strategies for enterprise database systems and set standards for operations, programming, and security
- Design and construct large relational databases. Integrate new systems with existing warehouse structure and refine system performance and functionality
- Provides technical/management leadership on major tasks or technology assignments
- Establishes goals and plans that meet project objectives. Has domain and expert technical knowledge
- Directs and controls activities for a client, having overall responsibility for financial management, methods, and staffing to ensure that technical requirements are met + Interactions involve client negotiations and interfacing with senior management + Decision making and domain knowledge may have a critical impact on overall project implementation
- May supervise others
- An active Secret security clearance is required
Preferred Qualifications:
- Master's degree or Ph.D. in a quantitative field
- Palantir Foundry certification (Data Science or Data Engineer track)
- Experience with Palantir AIP
- Experience with TypeScript and SQL
- CompTIA Security+ or equivalent DoD 8570/8140 baseline certification
IPTA is an Equal Opportunity Employer. All employment decisions are based on individual merit, including qualifications, experience, performance, and business needs, consistent with applicable federal laws and federal contracting requirements. IPTA provides equal opportunity and utilizes merit-based principles to make employment-related decisions.
#clearance
Salary Context
This $120K-$130K range is in the lower quartile 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 IPT Associates, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 34% below the category median. Disclosed range: $120K to $130K.
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.
IPT Associates AI Hiring
IPT Associates has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $130K - $160K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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