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About This Role
Position Summary
Systems Enginuity, Inc. (SEI) is seeking a Data Scientist / Analytics Engineer to support innovative FAA aviation safety research and the development of enterprise data and analytics capabilities. This early career position provides an opportunity to apply data science, statistical analysis, machine learning, and data engineering techniques to complex aviation safety and operational datasets.
Work collaboratively with aviation safety researchers, data scientists, and engineers to integrate, process, analyze, and visualize data related to aviation safety events, aircraft operations, system performance, weather, and National Airspace System safety.
You will develop analytical datasets, data processing pipelines, machine learning applications, and interactive visualizations within platforms such as Palantir Foundry and other enterprise data environments. Working in a cross-functional team, you will help transform complex aviation data into meaningful safety indicators, analytical products, and actionable insights that support FAA safety analysis, risk assessment, and oversight that protects the flying public.
Key Responsibilities
- Perform exploratory data analysis, statistical analysis, data mining, and time-series analysis to identify trends, patterns, anomalies, and factors affecting aviation safety and operational performance.
- Develop and apply statistical, machine learning, and natural language processing (NLP) techniques to analyze structured and unstructured aviation data, including safety reports, operational events, aircraft performance, and related information.
- Support development and evaluation of analytical models for aviation safety event classification, contributing factor identification, anomaly detection, and safety performance monitoring.
- Develop and maintain Python- and SQL-based data processing workflows to extract, clean, transform, integrate, and prepare complex aviation datasets for analysis.
- Support development of reusable data transformations, analytical pipelines, and curated datasets within enterprise analytics environments, including Palantir Foundry or comparable platforms.
- Evaluate data quality, completeness, consistency, and relationships across multiple datasets, and implement validation checks and documentation to support reliable and reproducible analytical results.
- Develop safety indicators, analytical summaries, interactive dashboards, and visualizations to communicate operational trends, emerging safety concerns, and other actionable findings.
- Document analytical methods, data transformations, model assumptions, validation results, and findings to support reproducibility, technical reviews, and ongoing use of analytical products.
Required Qualifications
- Bachelor's or Master’s degree in Data Science, Computer Science, Mathematics, Engineering, Physics, or related quantitative or technical field.
- Academic research, internships, capstone projects, graduate research, or professional experience in data science, analytics, software development, or a related field:
- 1+ year of experience with Bachelor’s degree, or
- No prior professional experience required with Master’s degree.
- Strong foundation in Python and familiarity with SQL and relational data concepts.
- Experience with or academic exposure to data analysis and scientific computing libraries such as Pandas, NumPy, Scikit Learn, or comparable tools.
- Understanding statistical analysis, exploratory data analysis, data preparation, and basic machine learning concepts.
- Familiarity with processing techniques for structured and unstructured data.
- Ability to develop well-organized, documented, and reproducible analytical code.
- Strong analytical and problem-solving skills, with demonstrated ability to work both independently and collaboratively in a team.
- Strong written and verbal communication skills, including the ability to document analytical methods and clearly present findings to technical and non-technical audiences.
Preferred Qualifications
- Experience with Palantir Foundry, Databricks, Microsoft Azure data services, Snowflake, or comparable enterprise data and analytics platforms.
- Familiarity with enterprise data pipelines, ETL/ELT processes, data integration, data quality management, or analytical workflow development.
- Experience with natural language processing (NLP) techniques such as text classification, regular expressions, text embeddings, or other methods for analyzing unstructured data.
- Exposure to machine learning techniques such as clustering, classification, regression, anomaly detection, or time-series modeling.
- Experience developing dashboards or data visualizations.
Our Interview Process
At SEI, we believe the interview process should be personal, transparent, and authentic. We do not use AI to conduct interviews. Selected candidates will meet directly with SEI Project Engineers and Hiring Managers in live online interviews to discuss qualifications, experience, and potential fit within our team and culture.
To ensure a fair evaluation process, we ask candidates not to use AI tools or other assistance during interviews. We evaluate each candidate based on their own knowledge, skills, experience, and problem-solving abilities.
Eligibility Requirements
Candidates who receive and accept an offer of employment with SEI must successfully complete a U.S. Federal Government public trust security process, which includes fingerprinting, a government conducted background investigation, and other required screenings.
Due to the requirements of the FAA contract supporting this position, applicants MUST BE a U.S. citizen or lawful PERMANENT resident and eligible to satisfy applicable U.S. Federal Government suitability and security requirements. SEI is unable to provide employment visa sponsorship for this position. Individuals working under or requiring temporary employment authorization, including F 1 CPT, OPT, or STEM OPT, are not eligible.
Pay: $67,000.00 - $82,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Health insurance
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Work Location: Remote
Salary Context
This $67K-$82K 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 Systems Enginuity, 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. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($74K) sits 61% below the category median. Disclosed range: $67K to $82K.
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.
Systems Enginuity AI Hiring
Systems Enginuity has 2 open AI roles right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $82K - $112K.
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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