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About This Role
The Principal Data Scientist will serve as the principal lead to provide accurate and quality analyses that translate data in sound organizational decisions. In this role you will provide strategic thought leadership and partner with other functional areas to deliver collaborative work products that align with divisional and enterprise strategy. You will own delivery of multiple large and/or complex data science and engineering projects.What You'll Do
- Lead in the development and testing of hypotheses across all functional data sets
- Lead requirements gathering sessions with business and technical staff to distill technical requirement from business requests
- Define and implement integrated data models, allowing integration of data from multiple sources
- Design and develop scalable, efficient data pipeline processes to handle data ingestion, cleansing, transformation, integration, and validation
- Define and implement data stores based on system requirements and consumer requirements
- Design and develop models to understand and solve multiple complex business solutions using a variety of methods including data mining, statistical analysis, predictive modeling and analysis, stochastic modeling, pattern recognition, probability analysis, and network analysis
- Design and implement production models leveraging technologies such Python, and R
- Leverage existing data sets and engineer new data sets including development of SQL queries, data integration and resolution of data quality issues
- Apply advanced conceptual understanding of Big Data systems to data set engineering and analytic model implementation
- Lead development and reviews of complex analytic models and algorithms
- Identify actionable insights, suggest recommendations, and influence business direction by effectively communicating results to cross functional groups
- Document and test analytic processes including performance and thorough data validation and verification
- Engage with enterprise business areas to incorporate feedback into audit process including but not limited to Payment Integrity, SIU, Provider, Behavioral Health, Med Policy, Claims, Pharmacy
- Articulate analytic methods and drive business outcomes with responsible business areas
- Consult with executive level leadership across the organization – with responsibility to drive CoC ideation for Commercial and Medicare
- Informally lead other analysts and synthesize information and make recommendations to executive leadership
What You Bring* Bachelor's degree or advanced degree (where required)
- 8\+ years of experience in related field.
- In lieu of degree, 10\+ years of experience in related field.
- Strong Analytic/Data Science skills:
- Experience with Snowflake
- Experience building AI Agents
- Understand Bell curve analysis
- Incorporate Industry benchmarks into analysis
- Perform outlier detection
- Strong healthcare experience with Payment Integrity/FWA business knowledge is required
- understanding of FWA concepts, CMS guidelines, Medical/Reimbursement Policy are all strongly preferred
Salary Range
At Blue Cross NC, we take great pride in a fair and equitable compensation package that reflects market\-price and our starting salaries are typically planned near the middle of the range listed. Compensation decisions are driven by factors including experience and training, specialized skill sets, licensure and certifications and other business and organizational needs. Our base salary is part of a robust Total Rewards package that includes an Annual Incentive Bonus\*, 401(k) with employer match, Paid Time Off (PTO), and competitive health benefits and wellness programs.
- *Based on annual corporate goal achievement and individual performance.*
$143,616\.00 \- $229,786\.00Skills
Big Data, Complex Data Analysis, Data Analysis, Data Engineering, Data Mining, Data Modeling Techniques, Data Science, Data Visualization, Machine Learning (ML), Predictive Analytics, Predictive Modeling, Python (Programming Language), Statistical Analysis, Statistical Models
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JOB ALERT FRAUD: We have become aware of scams from individuals, organizations, and internet sites claiming to represent Blue Cross and Blue Shield of North Carolina in recruitment activities in return for disclosing financial information. Our hiring process does not include text\-based conversations or interviews and never requires payment or fees from job applicants. All our career opportunities are published on https://bcbsnc.wd5\.myworkdayjobs.com/en\-US/BCBSNC. If you have already provided your personal information that you suspect is fraudulent activity, please report it to your local authorities. Any fraudulent activity should be reported to: [email protected].
Salary Context
This $143K-$229K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Blue Cross and Blue Shield of North Carolina, 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $143K to $229K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Blue Cross and Blue Shield of North Carolina AI Hiring
Blue Cross and Blue Shield of North Carolina has 1 open AI role right now. They're hiring across Data Scientist. Based in NC, US. Compensation range: $229K - $229K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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