Interested in this Data Scientist role at Concurrent Technologies Corporation?
Apply Now →Skills & Technologies
About This Role
SR DATA SCIENCE ENGINEER SPEC\-DATA SCIENTIST
Concurrent Technologies Corporation
Johnstown, PA or Telecommute
Minimum Clearance Required: N/A
Clearance Level Must Be Able to Obtain: N/A
Employee Background Check Required
Concurrent Technologies Corporation (CTC) an independent, nonprofit applied scientific research and development organization. Is seeking a Sr. Data Science Engineer Spec\-Data Scientist, you'll be part of the internal Information Technology team that keeps CTC's business operations running efficiently. Supporting employees across the organization, you'll contribute to a collaborative, customer\-focused environment where technology enables engineers, researchers, and business professionals to accomplish their mission. CTC values teamwork, innovation, continuous learning, and exceptional customer service, providing opportunities to expand your technical skills while building a rewarding career with an organization dedicated to making a meaningful impact.
Key Responsibilities:
- Collaborate with business leaders to identify high\-impact business problems and translate them into data science and data analysis projects.
- Collect, process, and analyze complex datasets from various sources to identify trends, patterns, and insights that inform business strategy.
- Develop and implement predictive models and machine learning algorithms to solve business problems, such as forecasting, customer segmentation, and optimization.
- Develop and maintain detailed, compelling dashboards and reports for both technical and non\-technical audiences using business intelligence (BI) tools.
- Perform exploratory data analysis (EDA) and statistical analysis to uncover patterns, trends, and anomalies.
- Create clear, compelling, and actionable data visualizations and reports to communicate complex findings to both technical and non\-technical audiences.
- Design and execute A/B tests and other experiments to measure the impact of different initiatives.
- Work with data engineers to build and improve data pipelines, ensuring data quality and accessibility.
Basic Qualifications:
- Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Computer Science, or Economics.
- 3–5 years of hands\-on experience in a Data Scientist or Senior Data Analyst role.
- Programming: Strong proficiency in Python (including libraries like Pandas, NumPy, and Scikit\-learn), SQL, and PySpark.
- Statistics and ML: Solid understanding of statistical analysis, data mining techniques, and machine learning algorithms (e.g., classification, regression, clustering, and decision trees).
- Communication: Excellent verbal and written communication skills with the ability to tell a story with data.
- Problem\-Solving: Proven ability to approach complex problems with a structured, analytical, and inquisitive mindset.
Preferred Qualifications:
- Experience with cloud\-based data platforms (e.g., AWS, Azure, GCP).
- Experience with big data technologies (e.g., Spark, Hadoop).
- Experience with data visualization and analysis tools (e.g., Tableau, Power BI, Matplotlib, R, SAS).
- Experience with natural language processing (NLP) or other forms of text analysis.
- Knowledge of experimental design and causal inference techniques.
- Experience in research \& development, government contracting, and/or highly regulated industry domains.
Why CTC?
- Our teams at CTC are passionate and thrive on collaboration in a team environment.
- When we encounter a difficult problem, we have a variety of talented and diverse employees that work together to solve the toughest challenges.
- 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/ (https://concurrent\-technologies\-corporation.breezy.hr/p/5214aee293b7\)
Staffing Requisition: SR\#2026\-0087
“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
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 Concurrent Technologies Corporation, 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.
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.
Concurrent Technologies Corporation AI Hiring
Concurrent Technologies Corporation has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.