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About This Role
Senior Data Science Engineer/Specialist
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Concurrent Technologies Corporation
Remote
Minimum Clearance Required: None
Clearance Level Must Be Able to Obtain: Secret
Employee Background Check Required
CTC Engineering and Manufacturing: Where Cutting\-Edge Innovation Meets Mission\-Critical Solutions
The CTC Engineering Division specializes in transforming cutting\-edge technologies into real\-world solutions. We harness the power of 3D printing, advanced joining techniques like friction stir welding, and cutting\-edge design tools to tackle complex challenges for a diverse set of clients.
As a Senior Data Science Engineer/Specialist, you will serve as a technical leader, applying advanced data science and mathematical techniques to solve complex and mission\-critical challenges for Department of Defense (DoD) clients. You will lead projects, mentor junior staff, and drive the development of innovative solutions that enhance decision\-making and operational efficiency. This role requires a high degree of technical expertise, strategic thinking, and the ability to work independently while managing multiple priorities.
Key Responsibilities:
Technical Leadership and Project Management:
- Lead the planning, execution, and delivery of complex data science and mathematics projects, ensuring alignment with client objectives and timelines.
- Provide technical leadership and mentorship to junior and mid\-level team members, fostering their professional growth.
- Collaborate with stakeholders to define project requirements, scope, and deliverables.
- Identify risks, develop mitigation strategies, and ensure the successful completion of projects.
Advanced Data Analysis and Modeling:
- Design and implement advanced statistical, predictive, and machine learning models to address complex client challenges.
- Perform sophisticated exploratory data analysis (EDA) to identify actionable insights and inform decision\-making.
- Develop and refine mathematical models for simulation, optimization, and forecasting tasks.
Algorithm Development and Innovation:
- Lead the design, development, and optimization of algorithms for data processing, analysis, and modeling.
- Validate and deploy algorithms in operational environments, ensuring scalability and reliability.
- Drive innovation by researching and applying cutting\-edge techniques in data science, artificial intelligence, and mathematics.
Tool and Technology Utilization:
- Utilize advanced programming skills in Python, R, MATLAB, or similar languages to develop custom solutions.
- Leverage data visualization tools (e.g., Tableau, Power BI, matplotlib) to create compelling and actionable insights.
- Integrate data science workflows into enterprise systems and processes to enhance operational efficiency.
Collaboration and Communication:
- Serve as a primary point of contact for clients, providing regular updates and ensuring satisfaction with deliverables.
- Prepare and deliver high\-quality technical reports, presentations, and documentation for diverse audiences, including senior leadership.
- Facilitate cross\-functional collaboration with multidisciplinary teams to achieve project goals.
Research and Development:
- Lead research initiatives to explore emerging data science techniques, tools, and technologies.
- Develop innovative methodologies and frameworks to address evolving client needs.
- Publish findings and contribute to the broader data science and mathematics community.
Support for DoD Projects:
- Provide expert analysis and recommendations to support DoD mission objectives and strategic decision\-making.
- Develop and implement advanced tools, processes, and methodologies to improve operational capabilities for DoD clients.
- Ensure all deliverables comply with DoD standards, policies, and security requirements.
Concurrent Technologies Corporation (CTC) is preparing to stand up a dedicated team to manage a new, high\-priority initiative for the U.S. Navy. This is a unique opportunity to help revitalize and strengthen a critical national security industrial base. As the manager of a large portfolio of innovative projects under a flexible contracting vehicle, CTC will be at the center of this mission. Our role will be to connect cutting\-edge solutions from across industry and academia to the Navy's most pressing challenges, accelerating technology adoption and ensuring our nation's defense industrial base has the capacity and capability for decades to come. This is a fast\-paced, dynamic program where you will play a foundational role in its success. We are seeking talented professionals to join us in this exciting and impactful work.
As a Senior Data Science Engineer, you will transform vast streams of project data into actionable intelligence and strategic insights for government leadership. You will develop the dashboards, analytics, and reports that provide radical transparency into the performance and impact of this national security program.
Required Qualifications:
- Bachelor's degree in Data Science, Mathematics, Computer Science, Engineering, or a related field.
- 4\+ years of relevant experience in data science, mathematics, or related fields.
- Advanced proficiency in programming languages such as Python, R, MATLAB, or similar.
- Strong expertise in statistical methods, machine learning techniques, and mathematical modeling.
- Experience with data visualization tools (e.g., Tableau, Power BI, matplotlib) and data engineering concepts.
- Proven ability to lead projects, manage teams, and deliver high\-quality results.
- Excellent analytical, problem\-solving, and critical\-thinking skills.
- Strong written and verbal communication skills, with experience presenting to technical and non\-technical audiences.
Preferred Qualifications:
- Master's or Ph.D. in Data Science, Mathematics, Computer Science, Engineering, or a related field.
- Extensive experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit\-learn).
- Advanced knowledge of database systems (e.g., SQL, NoSQL) and cloud computing platforms (e.g., AWS, Azure, Google Cloud).
- Experience working in DoD or government contracting environments, with a strong understanding of DoD standards and policies.
- Demonstrated ability to develop and implement innovative solutions to complex technical challenges.
- Experience with big data technologies (e.g., Hadoop, Spark) and distributed computing frameworks.
Work Environment/Conditions:
- Determined by management if applicable.
Why CTC?
- Mastering the future of manufacturing: Be at the forefront of technological advancements in advanced manufacturing.
- Innovate for impact: Your work will push the boundaries of what's possible, from next\-generation armaments to groundbreaking environmental solutions that directly impact critical missions and the lives of those protecting our country.
- Work alongside the best: Collaborate with a passionate team of engineers and scientists, united by a shared drive to excel and a commitment to delivering outstanding results.
- Leave your mark: CTC is more than just a job. It's a launchpad for your engineering dreams.
- + Competitive salary and benefits package.
+ Although our work at CTC is extremely important, we also recognize the need for our employees to maintain a proper mix of work and personal life.
+ Visit www.ctc.com (http://www.ctc.com) to learn more.
Join us! CTC offers exceptional career growth, cutting edge technology, educational opportunities, and recognition for quality work.
https://concurrent\-technologies\-corporation.breezy.hr/
Staffing Requisition: SR\# 2026\-0076
“We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability status, protected veteran status, or any other characteristic protected by law.”
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Concurrent Technologies Corporation, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Concurrent Technologies Corporation AI Hiring
Concurrent Technologies Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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