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About This Role
Job Title: Data Scientist l
Position Summary:
A Data Scientist l will be responsible for leveraging data to provide actionable insights, drive decision\-making, and optimize healthcare and business outcomes. The role will use their strong background in data science, a passion for healthcare, and the ability to work in a cross\-functional environment with product owners, data engineers, and business stakeholders.
Key Duties and Responsibilities:
Participate in all project phases from concept, requirements, development, test, and production support
- Explore and understand new big data and analytic technologies that enable faster and more efficient system operations, ultimately delivering timely insights and models.
- Ensure the security, confidentiality, and integrity of client information
Data Analysis \& Modeling:
- Create and refine predictive algorithms that support the suite of Star Quality product offerings as well as the overall client satisfaction initiatives for Pareto IntelligenceTM. Analyze large and complex healthcare datasets (e.g., clinical, operational, and patient data) to identify trends, patterns, and insights. Develop predictive models and advanced analytics to inform healthcare strategies and program decisions.
Machine Learning \& AI:
- Design, develop, and deploy machine learning algorithms and artificial intelligence solutions to improve insights, workflows, predictive diagnostics, outcomes, and operational efficiency.
- Explore and understand new big data and analytic technologies that enable faster and more efficient system operations, ultimately delivering timely insights and models.
Data Integration \& Cleaning:
- Work with diverse healthcare data sources, including electronic health records (EHR), claims data, clinical trial data, and sensor data, ensuring data is clean, accurate, and well\-prepared for analysis.
Collaboration with Cross\-Functional Teams
- Collaborate closely with healthcare professionals, product teams, engineers, and business stakeholders to define analytical goals, communicate insights, and implement data\-driven solutions.
- Participate in all project phases from concept, requirements, development, test, and production support
Visualization \& Reporting:
- Create clear, actionable reports and dashboards that communicate complex data findings to non\-technical stakeholders, including clinicians, executives, and healthcare administrators.
Continuous Improvement:
- Stay up\-to\-date with the latest advancements in data science, healthcare technology, and analytics tools. Contribute to the development of new methodologies and approaches for improving Star analytic capabilities and supporting client needs through data science.
Compliance \& Security:
Ensure all data analyses and machine learning models maintain the integrity of client information and comply with healthcare regulations such as HIPAA, data privacy laws, and security best practices.
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Education and Experience:
- Master’s degree or Ph.D. in Data Science, Computer Science, Statistics, Epidemiology, Biomedical Engineering, or a related field.
- Experience: 3\+ years of experience in data science, with a focus on healthcare or life sciences (or equivalent academic/research experience). Strong experience with machine learning algorithms, statistical modeling, and data analytics tools. Familiarity with healthcare data sources and formats, including EHR/EMR systems, claims data, HEDIS data, survey\-based data and prescription drug event data.
- Technical Skills: Proficiency in programming languages such as Python, R, or SQL. Experience working in JupyterHub environment and experience with data manipulation libraries (e.g., Pandas, NumPy,), machine learning frameworks (e.g., scikit\-learn, TensorFlow, PyTorch), and statistical analysis tools. Knowledge of data visualization tools such as Tableau, Power BI, or custom dashboards. Familiarity with cloud computing platforms (AWS, Google Cloud, Azure) is a plus.
- Analytical \& Problem\-Solving Skills: Strong ability to work with design experiments, and develop complex models to solve healthcare\-related challenges. Experience/exposure to big data technologies such as Hadoop, Spark, Docker, and Airflow preferred.
- Communication Skills: Ability to present complex data and technical findings in a clear, concise, and actionable manner to both technical and non\-technical audiences.
- Healthcare Knowledge: A solid understanding of healthcare systems, clinical workflows, and healthcare industry regulations (HIPAA, HITRUST, etc.) is a plus.
Preferred Qualifications
- Experience working with Medicare Advantage, Star Ratings, clinical decision support tools, healthcare prediction models, or patient outcome tracking systems.
- Familiarity with Natural Language Processing (NLP) and text mining techniques for processing unstructured healthcare data.
- Experience with healthcare reimbursement and claims data analysis.
Knowledge of health economics, epidemiology, or health policy.
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Knowledge, Skills, and Abilities:
- Complex Problem Solving \- Ability to identify and solve problems by reviewing related information, evaluating options and implementing solutions.
- Critical Thinking \- Ability to use logic and reason to identify strengths and weaknesses of alternative solutions, conclusions, or approaches to problems.
- Deductive Reasoning – Ability to apply general rules to specific problems to produce answers that make sense.
- Inductive Reasoning – Ability to combine pieces of information to form general rules or conclusions (includes finding a relationship among seemingly unrelated information or events).
- Quantitative and Analytical Skills \- Ability to apply quantitative and statistical analysis techniques to unstructured problems.
- Strong Communication Skills – Ability to communicate information and ideas orally and in writing clearly and concisely.
- Positive, flexible, and self\-motivated attitude, along with the ability to work as a collaborative team player.
- Excellent attention to details, accuracy and follow up.
- Ability to set priorities and work on multiple tasks simultaneously.
Excellent oral and written communication skills.
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The expected base salary for this position ranges from $72,000\-120,000 with a bonus target of up to 5% of the base salary. We do consider a wide range of factors when making offer decisions, including (but not limited to) the scope and responsibilities of the position, a candidate’s relevant skills, training, experience, education and where applicable, licensure and certifications obtained. We also consider organizational and market factors when making offer decisions.
Equal Employment Opportunity Statement:
Convey Health Solutions is an Equal Opportunity Employer committed to fostering an inclusive and diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other characteristic protected by applicable federal, state, or local laws.
Convey Health Solutions also provides reasonable accommodations to qualified individuals with disabilities in accordance with applicable laws. Applicants requiring accommodation during the application or interview process should contact the Human Resources department.
The Convey Health Solutions family of companies, including Pareto Intelligence™, delivers a powerful combination of purpose\-built technology, advanced analytics, and expert services to help health plans thrive in a complex, post, Affordable Care Act environment.
As a trusted partner to Medicare and commercial payers, we provide scalable, compliant solutions that span the entire member lifecycle, from enrollment and billing to risk adjustment, Stars performance, and member engagement. Pareto’s deep analytics and financial intelligence complement Conveys’ operational expertise, enabling our clients to improve performance, reduce costs, and create better healthcare experiences for millions of Americans, especially seniors and vulnerable populations.
Together, we help health plans scale smarter, grow stronger, and make healthcare work better for the people who need it most.
Learn more at www.ConveyHealthSolutions.com.
About Us
Convey Health Solutions, together with Pareto Intelligence™, delivers a powerful combination of purpose\-built technology, advanced analytics, and expert services to help health plans thrive in a complex, post\-Affordable Care Act environment.
As a trusted partner to Medicare and commercial payers, we provide scalable, compliant solutions that span the entire member lifecycle\-from enrollment and billing to risk adjustment, Stars performance, and member engagement. Pareto's deep analytics and financial intelligence complement Convey's operational expertise, enabling our clients to improve performance, reduce costs, and create better healthcare experiences for millions of Americans\-especially seniors and vulnerable populations.
Together, we help health plans scale smarter, grow stronger, and make healthcare work better for the people who need it most. Learn more at http://www.ConveyHealthSolutions.com
Salary Context
This $72K-$120K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Webster University, 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($96K) sits 53% below the category median. Disclosed range: $72K to $120K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Webster University AI Hiring
Webster University has 1 open AI role right now. They're hiring across Data Scientist. Based in Chicago, IL, US. Compensation range: $120K - $120K.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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