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About This Role
Pharmaceutical Strategies Group (PSG) relentlessly advocates for clients as they navigate complex and ever\-changing drug cost management challenges. Our mission, as we partner together with our clients, is to develop innovative drug management solutions that deliver exceptional insights with financial and clinical value.
PSG, an EPIC Company, is fueled and driven by capable, committed people who share common beliefs and values and "bring it" every day. For over 30 years PSG has been leading the way in pharmacy intelligence and growing as a result. This means we have opportunities for you to join our amazing team of talented individuals!
JOB OVERVIEW:
Artemetrx is PSG's robust data and analytics platform that drives measurable financial results for clients through advanced healthcare data, reporting, and analytics. The Data Scientist will support the continued evolution of Artemetrx by helping develop, implement, and scale analytical capabilities that improve client decision\-making, identify opportunities for cost and utilization management, and support the next generation of AI\-enabled drug management tools.
This role is designed for a highly analytical, curious, and technically capable individual who can work with a variety of healthcare data sets, including medical and pharmacy claims data, to uncover insights, build predictive models, and support data\-driven solutions. The Data Scientist will partner with clinicians, analysts, consultants, product leaders, and technical teams to translate business questions into analytical approaches, prepare and analyze complex data, and communicate findings clearly to technical and non\-technical stakeholders.
LOCATION: Remote \- Anywhere in the continental U.S.
WHAT WE'RE LOOKING FOR:
- At least 2\+ years of healthcare and/or pharmaceutical analytics experience
- Strong experience using SQL to write queries
- Python or R experience, in particular experience building data analysis, machine learning, or forecasting models
- Positive and outgoing disposition, eager to work as a team but also functions well working independently.
WHAT YOU'LL DO:
A detailed list of job duties includes (but is not limited to):
- Support the development and maintenance of predictive, statistical, and machine learning models that address business and client needs across Artemetrx initiatives.
- Analyze pharmacy and medical claims data as well as other healthcare data sets to identify trends, patterns, outliers, and opportunities that inform client strategy and improve health outcomes.
- Assist with AI roadmap initiatives
- Prepare, clean, transform, and validate data sets to ensure accuracy, completeness, and usability for advanced analytics, reporting, dashboards, and model development.
- Develop clean, reusable, and testable analytical code using established data team standards, libraries, and modules.
- Support Artemetrx through maintenance of reference tables and other data sets used within the product.
- Use programming languages such as SQL or Python to extract, manipulate, and analyze large and complex data sets.
- Collaborate with consultants, clinicians, analysts, product teams, data engineers, and other stakeholders to understand business problems, define requirements, and deliver actionable insights.
- Document analytical methods, assumptions, data definitions, model logic, and results to support transparency, repeatability, and quality control.
- Communicate analytical findings and recommendations through written summaries, presentations, dashboards, and stakeholder discussions.
- Stay current with developments in healthcare analytics, data science, machine learning, artificial intelligence, and the pharmacy benefits management industry.
WHAT YOU'LL BRING:
Requirements
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- 2\+ years of experience in data science, analytics, statistics, healthcare analytics, informatics, or a related analytical role preferred.
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science or a related quantitative discipline required; master's degree or relevant coursework preferred.
- Experience working with healthcare data, pharmacy/PBM data, medical claims, financial data, or similarly complex data sets
- Working knowledge of SQL for data extraction, querying, and data manipulation.
- Proficiency with Python or R for data analysis, statistical modeling, machine learning, or automation.
- Foundational understanding of statistics, predictive modeling, supervised and unsupervised learning, regression, classification, clustering, forecasting, and model evaluation techniques.
- Experience preparing, cleaning, validating, and analyzing raw data with a high degree of accuracy and attention to detail.
- Ability to translate business questions into analytical approaches and communicate results in a clear, concise, and practical manner.
- Strong problem\-solving skills, intellectual curiosity, resourcefulness, and ability to learn new tools, methods, and healthcare concepts quickly.
- Strong written and verbal communication skills, with the ability to collaborate effectively with technical and non\-technical stakeholders.
- Demonstrated ability to work independently and as part of a cross\-functional team while managing multiple priorities and deadlines.
- Proficiency with Microsoft Excel, Word, Outlook, and PowerPoint. Advanced Excel skills a plus.
- Ability for some travel, up to 10%.
Preferred Qualifications
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- Exposure to healthcare cost, utilization, clinical, pharmacy, PBM, or drug management analytics.
- Experience with predictive modeling, time\-series forecasting, claims analytics, anomaly detection, natural language processing, or AI\-enabled reporting.
- Familiarity with data privacy, data governance, HIPAA considerations, and secure handling of sensitive healthcare information.
- Experience supporting dashboards, reporting automation, reusable analytical workflows, or model deployment processes.
COMPENSATION:
*Exempt:* The base salary range for this position is $110,000 – $140,000\. This range represents the employer's good faith estimate of the compensation for this role at the time of posting.
Actual compensation will be determined based on factors such as experience, skills, training, certifications, education, internal equity, and market data. This role may also be eligible for bonuses and a comprehensive benefits package.
WHY EPIC:
EPIC has over 60 offices and 4,000 employees nationwide – and we're growing! It's a great time to join the team and be a part of this growth. We offer:
- Generous Paid Time off
+ Managed PTO for salaried/exempt employees (personal time off without accruals or caps); 22 PTO days starting out for hourly/non\-exempt employees; 12 company\-observed paid holidays; 4 early\-close days
- Generous leave time options: Paid parental leave, pregnancy disability and bonding leave, and organ donor/bone marrow donor leave
- Generous employee referral bonus program of $1,500 per hired referral
- Employee recognition programs for demonstrating EPIC's values plus additional employee recognition awards and programs (and trips!)
- Employee Resource Groups: Women's Coalition, EPIC Veterans Group
- Professional growth \& development: Mentorship Program, Tuition Reimbursement Program, Leadership Development
- Unique benefits such as Pet Insurance, Identity Theft \& Fraud Protection Coverage, Legal Planning, Family Planning, and Menopause \& Midlife Support
- Additional benefits include (but are not limited to): 401(k) matching, medical insurance, dental insurance, vision insurance, and wellness \& employee assistance programs
- 50/50 Work Culture: EPIC fosters a 50/50 culture between producers and the rest of the business, supporting collaboration, teamwork, and an inclusive work environment. It takes both production and service to be EPIC!
- EPIC Gives Back – Some of our charitable efforts include Donation Connection, Employee Assistance Fund, and People First Foundation
- We're in the top 10 of property/casualty agencies according to "Insurance Journal"
To learn more about EPIC, visit our Careers Page: https://www.epicbrokers.com/about/epic\-careers/.
*EPIC embraces diversity in all its various forms—whether it be diversity of thought, background, race, religion, gender, skills or experience. We are committed to fostering a work community where every colleague feels welcomed, valued, respected and heard. It is our belief that diversity drives innovation and that creating an environment where every employee feels included and empowered, helps us to deliver the best outcome to our clients.*
*California Applicants \- View your privacy rights at:* *https://www.epicbrokers.com/wp\-content/uploads/2025/01/epic\-ca\-employee\-privacy\-notice.pdf**.*
*Massachusetts G.L.c. 149 section 19B (b) requires the following statement: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.*
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Salary Context
This $110K-$140K range is in the lower quartile 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 EPIC Insurance Brokers & Consultants, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($125K) sits 35% below the category median. Disclosed range: $110K to $140K.
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.
EPIC Insurance Brokers & Consultants AI Hiring
EPIC Insurance Brokers & Consultants has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $140K - $140K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 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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