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About This Role
:
Sr. Associate Data Scientist\- Hybrid, Cary, North Carolina
We’re a leader in data and AI. Through our software and services, we inspire customers around the world to transform data into intelligence \- and questions into answers.
If you're looking for a dynamic, fulfilling career with flexibility and a world\-class employee experience, you'll find it here. We're recognized around the world for our inclusive, meaningful culture and innovative technologies by organizations like Fast Company, Forbes, Newsweek and more. About the job
The Applied AI \& Modeling (AAIM) team is looking for a Data Scientist to help advance a multi‑phase applied research and development effort focused on computer vision and machine learning for high‑impact real‑world data. Our team works at the intersection of advanced modeling, scalable AI systems, and domain‑driven problem solving, partnering closely with engineers and subject‑matter experts to turn emerging research into practical, measurable outcomes.
This is an exciting opportunity to build and evaluate state‑of‑the‑art vision models while working on problems that demand both technical depth and rigor. You will contribute to the evolution of existing prototypes into more robust, scalable solutions, gaining hands‑on experience with 3D vision, attention mechanisms, and modern deep learning architectures in a collaborative environment. This role is well‑suited for someone who wants to grow as an applied data scientist, learn how advanced AI systems are developed responsibly, and see their work directly influence the next stage of real‑world AI innovation.
As a Sr. Associate Data Scientist, you will:
- Design, develop, and evaluate machine learning and computer vision models to solve complex, real‑world problems using large and diverse datasets.
- Apply and extend modern deep learning techniques (e.g., convolutional models, 3D vision, attention mechanisms, transformer‑based approaches) to improve model accuracy, robustness, and scalability.
- Analyze model performance using appropriate quantitative metrics, identify failure modes, and iterate on solutions based on experimental findings.
- Collaborate with fellow data scientists, software engineers, and cross‑functional partners to integrate models into end‑to‑end analytical workflows.
- Contribute to well‑documented, reproducible modeling pipelines and clearly communicate insights, tradeoffs, and results to technical and non‑technical audiences.
- Ensure all applicable security policies and development processes are followed to support the organization’s secure and responsible software development goals.
- Embrace curiosity, passion, authenticity, and accountability \- our values that guide how we work, learn, and innovate together.
Required qualifications
- Master’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field
- Demonstrated experience applying computer vision and machine learning techniques to real‑world problems, including tasks such as image analysis, feature extraction, model training, and performance evaluation.
- Hands‑on experience with at least one modern deep learning or computer vision framework (e.g., Python‑based frameworks commonly used for CNN‑ or vision‑based modeling).
- Experience working with real‑world datasets, including data preparation, model evaluation using quantitative metrics, and result interpretation.
- Ability to analyze results, troubleshoot models, and clearly communicate technical findings to both technical and non‑technical audiences.
- An equivalent combination of related education, training, and experience may be considered in place of the above qualifications.
Additional competencies, knowledge and skills
Key competencies
- Analytical Thinking – Ability to break down complex, ambiguous problems into structured analytical tasks, evaluate alternative approaches, and use data to support sound technical decisions.
- Collaboration – Ability to work effectively with cross‑functional partners, including data scientists, engineers, and domain experts, contributing constructively in a team‑based environment.
- Learning Agility – Willingness and ability to quickly learn new methods, tools, and domains, and apply new knowledge to evolving technical challenges.
Additional skills and experience (nice to have)
- Experience with computer vision techniques for image segmentation, detection, or classification.
- Exposure to 3D modeling, such as 3D convolutional networks or multi‑dimensional image analysis.
- Familiarity with attention mechanisms or transformer‑based vision models.
- Experience working in collaborative research and innovative environments.
World\-class benefits
Highlights include...* Comprehensive medical, prescription, dental and vision plans.
- Medical plan options include:
+ PPO with low annual deductible and copays.
+ HDHP combined with a health savings account with a contribution from SAS (no access to on\-site health care center).
- Onsite Health Care Center (HQ) that’s free to employees and family members enrolled in the PPO plan. There's a pharmacy too! Not local to HQ? The pharmacy will ship prescriptions for no additional charge!
- An industry\-leading 401k plan.
- Tuition Assistance Program and programs and resources to support your development
- Generous time away including vacation time, a variety of paid holidays, and our much\-loved U.S. Winter Wellness Break between December 25 and January 1\.
- Volunteer Time Off, parental leave and unlimited paid sick days.
- Generous childcare benefits for all full\-time employees.
You are welcome here.
At SAS, it’s not about fitting into our culture – it’s about adding to it. We believe our people make the difference. Our inclusive workforce brings together unique talents and inspires teams to create amazing software that reflects the diversity of our users and customers. Additional Information:
To qualify, applicants must be legally authorized to work in the United States, and should not require, now or in the future, sponsorship for employment visa status. SAS is an equal opportunity employer. All qualified applicants are considered for employment without regard to any characteristic protected by law. Read more: Know Your Rights.
Resumes may be considered in the order they are received. SAS employees performing certain job functions may require access to technology or software subject to export or import regulations. To comply with these regulations, SAS may obtain nationality or citizenship information from applicants for employment. SAS collects this information solely for trade law compliance purposes and does not use it to discriminate unfairly in the hiring process. *SAS only sends emails from verified “sas.com” email addresses and never asks for sensitive, personal information or money. If you have any doubts about the authenticity of any type of communication from, or on behalf of SAS, please contact* *[email protected].*
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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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At SAS, 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 $198,000 based on 808 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 $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
SAS AI Hiring
SAS has 2 open AI roles right now. They're hiring across Data Scientist. Based in Cary, NC, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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