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About This Role
Company :
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Highmark Health
Job Description :
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JOB SUMMARY
\*\*\*Entirely Remote Work From Anywhere Role!\*\*\*
We are seeking an experienced data scientist to join our AI Services and Platforms (AIPS) team and drive the development of innovative AI (generative and predictive) based solutions for our enterprise stakeholders. The AIPS team is a critical enabler of AI\-driven innovation within the company, functioning as an internal service provider to deliver cutting\-edge AI solutions and infrastructure. In this role, you will be instrumental in translating business needs into AI solutions that align with strategic goals and enable long\-term success across the organization.
As a Senior Data Scientist , you will leverage your expertise in AI and data to solve diverse business challenges. You will design and build best\-in\-class AI and agent evaluation capabilities , acting as a technical subject matter expert within cross\-functional teams including Business Stakeholders, Data Science, Engineering, Responsible AI \& Governance, and Product. You will be essential in laying the foundational R\&D work for responsible and scalable AI solutions .
The ideal candidate will have a proven track record of successfully applying Generative AI and Predictive AI to solve complex business problems. This role will play a pivotal role in driving our organization's digital transformation by harnessing the power of Generative AI and data science to extract valuable insights from vast and diverse datasets. We are looking for an individual with a deep understanding of AI techniques , as well as strong data science and problem\-solving skills . A successful candidate will have experience with designing and implementing end\-to\-end evaluations of AI/ML solutions , incorporating principles of Responsible AI and building robust evaluation pipelines. Candidates should also have a strong interest in research and development and be eager to explore new ideas and contribute to the advancement of AI, GenAI, and agentic AI in the healthcare technology space . This role requires a professional dedicated to staying on top of the latest Gen AI research like RAG, fine\-tuning, agents, etc. , and is adept at applying them to create innovative products and services. The candidate will also be an excellent communicator , who is able to clearly articulate their findings to business stakeholders.
ESSENTIAL RESPONSIBILITIES
- Work directly with the business to understand their business processes and aims, then identify how analytical solutions could help deliver value for them. This would include being accountable for:
- Outlining complex new use cases \+ creating high level impact estimates
- Identifying data elements needed and where to get them (including proxies)
- Assembling data sets independently using knowledge of Highmark operational and analytic data structures
- Deliver the analytical solution to a complex business problem
- Documenting objectives, assumptions and processes in line with our standards
- Select and apply the appropriate advanced modeling/machine learning techniques to these data sets to deliver business insight, ensuring that the final analysis is well researched, accurate, and documented. This requires: Experience with a substantial number of advanced analytical techniques and proficiency in a few, evidenced by in\-depth knowledge and delivery record (for example regression models, tree\-based learning, neural networks, clustering techniques, natural language processing)
- Consult with the business to contextualize and translate the results of our analysis in a form which the business can understand and act upon. This will include: Written reports, presentation and data visualizations, and draws clear lines between the high\-level problem specifications for colleagues and stakeholders, the analyses performed, and how the results link directly back to business objectives, and work with colleagues to deliver implementation which drives frontline workflow.
- Plan, prepare and deliver/coordinate all elements of the analysis at the direction of a manager in such a way that it is delivered on time, to a high standard and ready to implement on a production basis (including dissemination through the Organization's user systems). This includes identifying the best route to implementation (developing the analytical solution accordingly).
- Research self\- directed new analytical skills and approaches, building relationships internally and externally to transfer knowledge and maintain their position as subject matter experts, consult with fellow data scientists and analysts to guide analysis and deliver larger projects.
- Other duties as assigned or requested.
EDUCATION
Required
- Master's Degree in Analytics, Mathematics, Physics, Computer and Information Science, Engineering or closely related field OR Bachelor's degree in Analytics, Mathematics, Physics, Computer and Information Science, Engineering plus 3 years of experience in lieu of Master's degree
Preferred
- None
EXPERIENCE
Required
- 2 years Data Science
- 0 (if PhD Education)
Preferred
- Ability to stay abreast of the latest research in the field and identify new opportunities for innovation, experiment with new approaches and contribute to the development of novel applications of Generative AI and AI agents.
- 3\+ years of experience with using Python for AI/ML development
- Any experience with developing and deploying AI/ML models on AWS Sagemaker, Azure ML, Vertex AI (preferred)
- Experience or knowledge in building applications that leverage LLMs as a service, like Vertex AI LLM APIs (Gemini), Azure OpenAI API, Amazon Bedrock, OpenAI API (e.g., GPT\-3\.5, GPT\-4\), and/or Anthropic Claude API.
- Familiarity with LLM orchestration tools like LangChain, llamaindex for developing RAG based applications.
- Experience with designing and implementing end\-to\-end evaluations of AI/ML solutions, incorporating principles of Responsible AI and building evaluation pipelines
- Ability to think creatively and collaborate to apply AI to solve business problems with multiple capabilities, including experience design, change management, and process reengineering.
- Excellent communication and presentation skills to explain AI solutions to stakeholders
LICENSES AND CERTIFICATIONS
Required
- None
Preferred
None
*
SKILLS
- Analysis of business problems/needs
- Analytical and Logical Reasoning/Thinking
- Collaborative Problem Solving
- Data Analysis with SQL, BigQuery
- Statistical Analysis with Python, R
- Written \& Oral Presentation Skills
- Basic proto\-typing/front end skills
Language (other than English)
None
Travel Requirement
0% \- 25%
PHYSICAL, MENTAL DEMANDS AND WORKING CONDITIONS
Position Type
Office\-Based
Teaches / trains others regularly
Rarely
Travel regularly from the office to various work sites or from site\-to\-site
Never
Works primarily out\-of\-the office selling products/services (sales employees)
Never
Physical work site required
No
Lifting: up to 10 pounds
Frequently
Lifting: 10 to 25 pounds
Ocassionally
Lifting: 25 to 50 pounds
Rarely
*Disclaimer:* *The job description has been designed to indicate the general nature and essential duties and responsibilities of work performed by employees within this job title. It may not contain a comprehensive inventory of all duties, responsibilities, and qualifications required of employees to do this job.*
*Compliance Requirement:* *This position adheres to the ethical and legal standards and behavioral expectations as set forth in the code of business conduct and company policies.*
*As a component of job responsibilities, employees may have access to covered information, cardholder data, or other confidential customer information that must be protected at all times. In connection with this, all employees must comply with both the Health Insurance Portability Accountability Act of 1996 (HIPAA) as described in the Notice of Privacy Practices and Privacy Policies and Procedures as well as all data security guidelines established within the Company’s Handbook of Privacy Policies and Practices and Information Security Policy.*
*Furthermore, it is every employee’s responsibility to comply with the company’s Code of Business Conduct. This includes but is not limited to adherence to applicable federal and state laws, rules, and regulations as well as company policies and training requirements.*
Pay Range Minimum:
$102,700\.00
Pay Range Maximum:
$164,600\.00
*Base pay is determined by a variety of factors including a candidate’s qualifications, experience, and expected contributions, as well as internal peer equity, market, and business considerations. The displayed salary range does not reflect any geographic differential Highmark may apply for certain locations based upon comparative markets.*
Highmark Health and its affiliates prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities and prohibit discrimination against all individuals based on any category protected by applicable federal, state, or local law.
We endeavor to make this site accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact the email below.
For accommodation requests, please contact HR Services Online at HRServices@highmarkhealth.org
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Salary Context
This $102K-$164K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Highmark Health, 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. This role's midpoint ($133K) sits 31% below the category median. Disclosed range: $102K to $164K.
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.
Highmark Health AI Hiring
Highmark Health has 1 open AI role right now. They're hiring across Data Scientist. Based in PA, US. Compensation range: $164K - $164K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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
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