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About This Role
Job Description:
As a Staff Data Scientist, you will play a pivotal role in our Data Science and Machine Learning (DSML) team, driving high\-impact projects that shape our product experience and capabilities. You will leverage your deep expertise in data science, applied AI, and machine learning to solve complex challenges, enhance our product offerings, and deliver actionable insights that propel our business forward. This role requires a blend of technical mastery, strategic thinking, and leadership to foster innovation and deliver results.
Who you’re committed to being:
- You enjoy learning and are open to new ways of doing things.
- You are not afraid to be yourself, experiment, make mistakes and learn from them, ask questions, or voice your concerns.
- When communicating you are self\-aware, insightful, and proactive.
- You are a team member first and individual contributor second. You are aware that high\-performing teams are only as strong as their weakest link.
- You believe in continuous improvement and request frequent feedback from others.
What you’ll do:
- Lead and execute end\-to\-end data science and AI projects, including problem definition, data exploration, model development, deployment, and evaluation.
- Collaborate with product managers, engineers, and business stakeholders to define project objectives, align on priorities, and ensure successful outcomes.
- Develop and implement advanced machine learning and AI models and algorithms to drive product innovation, improve user experience, and optimize business processes.
- Coach, mentor and develop data scientists, fostering a culture of technical excellence and continuous learning.
- Stay abreast of industry trends, emerging technologies, and best practices in data science, machine learning, and AI; evaluate and advocate for their adoption within the team and organization.
- Communicate complex data\-driven insights and recommendations to technical and non\-technical stakeholders through clear visualizations and presentations.
Experience you’ll bring:
- Deep understanding of machine learning algorithms, large language models (LLMs), and statistical methods, with practical experience deploying them in production.
- Proficiency in Python and interactive development environments (e.g., Jupyter or similar).
- Experience with modern data platforms for scalable data processing and analytics (e.g., Snowflake or other cloud data warehouses; dbt or similar frameworks; Airflow or other orchestration tools; Kubernetes or comparable container systems).
- Experience with modern ML platforms for development, deployment, and operations (e.g., PyTorch/TensorFlow; MLflow or similar experiment tracking; Hugging Face or vector databases for LLMs; MLOps practices at scale).
Requirements:
- Advanced degree (Ph.D. or M.S.) in Computer Science, Statistics, Mathematics, Engineering, or a related field.
- 7\+ years of experience in data science, applied AI, or machine learning, with a proven track record of delivering impactful, product\-oriented projects.
- Familiarity with model deployment and serving approaches, including use of large compute resources.
- Experience with data visualization and lightweight app frameworks (e.g., Streamlit or comparable) for communicating insights.
- Strong foundation in software engineering best practices (e.g., Git/GitLab, CI/CD, reproducibility).
- Demonstrated ability to solve complex problems, mentor others, and influence across technical and product teams with clear communication and collaborative leadership.
- This is a remote role; however, applicants located within 45 miles of our Westlake/Dallas, TX office should expect to work on\-site Tuesday through Thursday, with remote flexibility on Mondays and Fridays. This approach enables more effective collaboration, quicker decision\-making, and a stronger culture, while still providing flexibility.
Why you’ll love working here:
- We’re a blended workplace, where team members work remotely or in a hybrid setup depending on their role and location
- We’re mission driven and guided by our culture pillars
- We have a strong commitment to diversity and belonging
- We cultivate a culture of trust, autonomy, and collaboration
- We’re lifelong learners and champion team member growth and advancement
- We’ve got you covered \- team member benefits include competitive compensation packages, medical coverage, unlimited PTO, wellness reimbursements, Pluralsight subscription, professional development funds and more.
About us:
Pluralsight provides the only learning platform dedicated to accelerating the technology skills and capabilities of today’s tech workforce. Thousands of companies, government organizations and individuals around the world rely on Pluralsight to support critical technology skill development in areas that are crucial to innovation including artificial intelligence, cloud computing, cybersecurity, software development, and machine learning. Pluralsight provides highly curated content developed by vetted technology experts, industry leading skill assessments, and hands on, immersive learning experiences designed to help individuals skill\-up faster.
*Physical Requirements:*
*This role is primarily performed in an office or home office setting and involves standard computer\-based work.*
*EEOC Statement \& Accommodations Statement:*
*Bring yourself.* *Pluralsight is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or veteran status. We also consider qualified applicants with criminal histories, consistent with EEOC guidelines and local laws.*
*If you need an accommodation to apply, interview, or perform essential job functions,*
*The annual US base salary range for this role is $153,900 \- $220,000 USD. Actual compensation will depend on location, skills, experience, and other factors. Additional benefits and bonuses may apply.*
*Applications must be submitted within 90 days after the initial posting date to be considered.*
*Recruiting Scam Notice:*
*Please be aware of recruiting scams. We’ll only contact you from an @pluralsight.com email or verified channels. We never ask for sensitive personal info or payments as part of the hiring process. All openings are posted on our Careers page.*
### About Us
Pluralsight provides the only learning platform dedicated to accelerating the technology skills and capabilities of today’s tech workforce. Thousands of companies, government organizations, and individuals around the world rely on our platform to support critical technology skill development in areas that are crucial to innovation, including artificial intelligence, cloud computing, cybersecurity, software development, and machine learning.
We offer highly curated content developed by vetted technology experts, industry leading skill assessments, and hands\-on, immersive learning experiences designed to help individuals skill\-up faster.
Salary Context
This $153K-$220K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Pluralsight, 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. This role's midpoint ($186K) sits 6% below the category median. Disclosed range: $153K to $220K.
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.
Pluralsight AI Hiring
Pluralsight has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US. Compensation range: $195K - $220K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 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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