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About This Role
Position Title: SeniorData Scientist
Requisition ID: 1814
Position Location: Remote
Position Reports To: Associate Data Science Manager
Supervises Others: No
At Trident Systems, we believe that strong engineering principles are fundamental to driving innovation and solving complex problems. We promote a culture characterized by rigorous engineering practices and a commitment to continuous improvement. This is achieved by leveraging our organization's collective expertise through collaborative development processes, which include thorough design and peer reviews. We can deliver innovative, high\-performance solutions that meet our customers' evolving requirements by integrating our specialized knowledge in aerospace electronic systems with appropriately scaled development methodologies.
We are a mission partner supporting DoD, Intelligence Community, and Civil space customers. We develop complex, radiation effects mitigated, designs that balance competing requirements in modern space programs, delivering cutting\-edge solutions that enable our customers to achieve more in space
Position Summary
This position will be part of the Predictive Maintenance and Logistics Team supporting Department of Defense (DoD) customers on projects that leverage advanced technologies including machine learning, artificial intelligence (ML/AI), and cloud infrastructure. The Senior Data Scientist will contribute to the development, evaluation, and deployment of data\-driven models focused on time\-series telemetry and operational system data.
The role emphasizes hands\-on Python development using modern machine learning frameworks, working with real\-world sensor and telemetry data to build models that support health monitoring, anomaly detection, forecasting, and decision support. The Senior Data Scientist will collaborate closely with engineers, data visualization specialists, and program leadership to transition analytical solutions from research into operational environments. U.S. citizenship and the ability to obtain a security clearance are required.
Duties and Responsibilities
- Develop and evaluate machine learning models using Python to analyze time\-series and telemetry data.
- Perform data exploration, feature engineering, and preprocessing on structured and semi\-structured datasets.
- Utilize natural language processing and probabilistic modeling to extract data from maintenance records.
- Develop Physics\-informed models based on the known relationships of signals for systems with low historical data available.
- Implement algorithms for anomaly detection, predictive modeling, and trend analysis in operational data.
- Support model validation, performance evaluation, and documentation.
- Contribute to the deployment of models into production and embedded environments in coordination with engineering teams.
- Collaborate with data visualization and software teams to integrate model outputs into dashboards and decision\-support tools.
- Document methodologies, assumptions, and results for technical and non\-technical stakeholders.
- Stay current with emerging machine learning techniques and apply best practices to ongoing projects.
- Ability to support travel or off\-site work, as needed
- Perform other duties as assigned.
Required Qualifications
- Master’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or a related field with 4\+ years of experience, or Bachelor’s degree in a related field with 6\+ years of relevant experience (or equivalent experience in lieu of a degree).
- Proficiency in Python for data analysis and machine learning.
- Experience with common machine learning libraries (e.g., scikit\-learn, PyTorch, TensorFlow, or similar).
- Experience with natural language processing techniques and toolchains.
- Familiarity with time\-series data, telemetry, or sensor\-based datasets.
- Working knowledge of data analysis tools such as NumPy, Pandas, and SciPy.
- Ability to communicate technical concepts clearly in both written and verbal form.
- Strong analytical thinking and problem\-solving skills.
- Must be a U.S. Citizen with the ability to obtain and maintain a security clearance.
Preferred Qualifications
- Experience applying machine learning to predictive maintenance, health monitoring, or operational analytics on time\-series data systems.
- Experience applying advanced techniques to extract meaningful data from bulk maintenance records to inform future maintenance actions.
- Exposure to model deployment concepts (e.g., batch pipelines, APIs, or edge/embedded environments).
- Familiarity with cloud platforms or big data environments.
- Experience working in a government or DoD context.
- Familiarity with the relationship between engine signals and performance.
- Knowledge of version control (e.g., Git) and collaborative development workflows.
Pay Information
Full\-Time Salary Range: $78,900 \- $187,200
Please Note: Actual compensation offered will be determined based on several factors including, but not limited to, relevant experience, skills, education, certifications, internal equity, business considerations, and geographic location where applicable.
Benefits
Hired applicants may be eligible for benefits including but not limited to:
- Health benefits
+ Medical
+ Dental
+ Vision
+ Basic life with AD\&D
+ Short term disability
+ Long term disability
+ Ancillary (Voluntary life with AD\&D, accident, critical illness, hospital, and pet)
+ Spending accounts (HSA, FSA, and DCFSA)
- Paid time off
- Holidays
- 401(k) (including company match)
- Tuition reimbursement
- Leaves (Parental, maternity, and military)
- Annual discretionary bonus (for eligible roles)
- Potential annual bonus
*Trident Systems reserves the right to change or assign other duties to this position.*
*Trident Solutions is an affirmative action and equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. To request reasonable accommodation to participate in the job application or interview process, please contact* *[email protected]**.*
*Pay Transparency: The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60\-1\.35(c)*
Salary Context
This $78K-$187K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Trident Systems Inc, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($133K) sits 31% below the category median. Disclosed range: $78K to $187K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Trident Systems Inc AI Hiring
Trident Systems Inc has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $187K - $187K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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