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About This Role
Team CATHEXIS elevates the government contracting experience through rapid response, deep skill, and thoughtful problem\-solving and communication. Our core capabilities are our top\-tier program and project management, data analytics, and audit services, the backbone of which is our integrated approach to operational excellence.
You worked hard to get to where you are. You strive to make every day better than the day before. So do we. Team CATHEXIS operates with an all\-in mindset. We are working together to create a company that supports our shared values and individual goals. Our values are centered around leading with integrity, owning the outcome, growing together, and moving with purpose in everything we do for our employees, customers, partners, and communities. We believe success is best when we listen and lead with empathy; model high standards of ethics to provide a rewarding candidate experience; work hard, have fun, and appreciate the strengths we all bring to the team; and empower our employees to create innovative and trusted results.
We are looking for a dynamic Data Scientist/ML Engineer to join our team. The Data Scientist/ML Engineer will work directly with data scientists, software engineers, and subject matter experts in the definition of new analytics capabilities able to provide our federal customers with the information they need to make proper decisions and enable their digital transformation.
ResponsibilitiesThe responsibilities include, but are not limited to:
- Research, design, implement, and deploy Machine Learning algorithms for enterprise applications.
- Assist and enable federal customers to build their own applications.
- Contribute to the design and implementation of new features.
Requirements
- An active Secret or above clearance is required.
- Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or equivalent fields required.
- MS or PhD in Computer Science, Electrical Engineering, Statistics, or equivalent fields preferred.
- Minimum 3\-5 years relevant work experience preferred.
- Excellent programming skills in Python.
- Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning).
- Strong mathematical background (linear algebra, calculus, probability, and statistics).
- Experience with scalable ML (MapReduce, streaming).
- Ability to drive a project and work both independently and in a team.
- Smart, motivated, can\-do attitude, and seeks to make a difference.
- Excellent verbal and written communication.
- Real passion for developing team\-oriented solutions to complex engineering problems.
- Thrive in an autonomous, empowering and exciting environment.
- Great verbal and written communication skills to collaborate multi\-functionally and improve scalability.
- Interest in committing to a fun, friendly, expansive, and intellectually stimulating environment.
- Convey highly technical concepts and information in written form to technical and non\-technical audiences.
- The ability to work on multiple concurrent projects is essential.
- Strong self \-motivation and the ability to work with minimal supervision.
- Must be a team\-oriented individual, energetic, result \& delivery oriented, with a keen interest on quality and the ability to meet deadlines.
- Ability to work in an agile environment.
Desired Skills* Hands\-on experience deploying and operating applications using IaaS and PaaS on major cloud providers, such as Amazon AWS, Microsoft Azure, or Google Cloud Services.
- Proficient in leveraging modern LLM tools to accelerate development workflows and enhance code quality.
- Experience with deep learning, natural language processing, computer vision, or reinforcement learning.
Benefits
CATHEXIS offers competitive compensation packages to all eligible employees. Our goal is to provide a compensation package that reflects the value you bring to our team, is competitive with national average market rates, and promotes your financial security and personal well\-being. The annual salary range for this role is $120,000\- $150,000\. Please note that the salary information provided is a general guideline. CATHEXIS considers various factors in its final offer, including location, qualifications, experience, and skills.
- Performance Bonuses
- Medical Insurance
- Dental Insurance
- Vision Insurance
- 401(k) Plan (Traditional and ROTH)
- Life Insurance (Basic, Voluntary \& AD\&D)
- Paid Time Off
- 11 Federal Holidays
- Parental Leave
- Commute Benefits
- Short Term \& Long Term Disability
- Training \& Development
- Wellness Program
- Community Outreach Initiatives
*CATHEXIS is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability EEO IS THE LAW. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact the Recruiting Department [email protected]*
Salary Context
This $120K-$150K range is below the median for Data Scientist roles in our dataset (median: $162K across 211 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 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Cathexis, 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 $200,000 based on 697 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($135K) sits 32% below the category median. Disclosed range: $120K to $150K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Cathexis AI Hiring
Cathexis has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Redwood City, CA, US, Tysons, VA, US. Compensation range: $104K - $150K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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