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About This Role
Job Description:
Artificial Intelligence; Advanced Technology; The very best in patient care. With decades of expertise, RadNet is *Leading Radiology Forward*. With dynamic cross\-training and advancement opportunities in a team\-focused environment, the core of RadNet’s success is its people with the commitment to a better healthcare experience. When you join RadNet as an *Analytics Data Scientist*, you will be joining a dedicated team of professionals who deliver quality, value, and access in the 21st century and align all stakeholders\- patients, providers, payors, and regulators to achieve the best clinical outcomes.
You Will:
*Predictive \& Prescriptive Analytics*
- Develop analytical models that drive business outcomes:
- Design and build predictive models for forecasting, demand planning, and capacity optimization
- Develop risk and anomaly detection systems for operational and clinical metrics
- Create scenario analysis and "what\-if" models to support strategic decision\-making
- Build decision\-scoring frameworks that quantify trade\-offs and recommend actions
- Translate business problems into analytical frameworks with measurable outcomes
*Machine Learning \& Model Development*
- Build, validate, and deploy ML models as enterprise assets:
- Develop feature engineering pipelines using governed data from the Gold Layer
- Train, validate, and evaluate machine learning models using appropriate techniques and frameworks
- Implement model monitoring for drift, bias, and performance degradation
- Create model documentation including methodology, assumptions, limitations, and explainability
- Partner with AI Engineers to deploy models into production environments
*Statistical Analysis \& Research*
- Apply rigorous analytical methods to answer business questions:
- Conduct exploratory data analysis to identify patterns, trends, and insights
- Apply statistical methods (regression, hypothesis testing, time series analysis) to validate findings
- Design and analyze experiments (A/B tests, randomized trials) to measure intervention impacts
- Quantify uncertainty and communicate confidence levels in analytical outputs
- Stay current with advances in data science, ML, and AI methodologies
*AI Measurement \& Effectiveness*
- Measure and optimize the impact of AI initiatives
- Define metrics and KPIs to measure AI model effectiveness and business impact
- Track and report on model performance in production environments
- Evaluate AI outputs for accuracy, bias, and fitness for purpose
- Provide feedback to improve AI systems based on real\-world performance
- Support responsible AI practices including fairness testing and transparency
*Stakeholder Collaboration \& Communication*
- Partner with business teams to deliver analytical value
- Collaborate with business stakeholders to understand problems and translate them into analytical projects
- Present findings and recommendations to technical and non\-technical audiences
- Create visualizations and narratives that make complex analyses accessible and actionable
- Partner with BI teams to operationalize analytical insights into dashboards and reports
- Coach and mentor analysts on statistical thinking and advanced analytical techniques
If You Are:
- Passionate about patient care and exercise sound judgement and an ability to remain professional in all situations.
- You demonstrate effective and professional communication, interpersonal skills and respect with patients, guests \& colleagues.
- You have a structured work\-approach, understand complex problems and you are able to prioritize work in a fast\-paced environment.
To Ensure Success in This Role, You Must Have:
- Master’s or Ph.D. in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field; or Bachelor’s with equivalent experience
- 3\+ years of experience in data science, machine learning, or advanced analytics roles
- Strong proficiency in Python and data science libraries (pandas, NumPy, scikit\-learn, statsmodels)
- Experience with machine learning frameworks (PyTorch, TensorFlow, XGBoost, LightGBM)
- Solid foundation in statistics including regression, hypothesis testing, experimental design, and time series analysis
- Proficiency in SQL for data extraction and manipulation
- Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or BI tools)
- Excellent communication skills with ability to explain complex analyses to non\-technical stakeholders
*Preferred*
- Experience with cloud ML platforms (GCP Vertex AI, AWS SageMaker, Azure ML)
- Knowledge of MLOps practices and model deployment pipelines
- Healthcare analytics experience including clinical, operational, or revenue cycle domains
- Experience with causal inference, Bayesian methods, or optimization techniques
- Familiarity with LLMs, NLP, or generative AI applications
- Experience with big data technologies (Spark, BigQuery, Databricks)
- Track record of deploying models that delivered measurable business impact
We Offer:
- Comprehensive Medical, Dental and Vision coverages.
- Health Savings Accounts with employer funding.
- Wellness dollars
- 401(k) Employer Match
- Free services at any of our imaging centers for you and your immediate family.
Pay Range: $95,000\.00 – $150,000\.00 per year
Pay Range: USD $95,000\.00 \- USD $150,000\.00 /Yr.
Salary Context
This $95K-$150K range is in the lower quartile 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 RadNet, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($122K) sits 38% below the category median. Disclosed range: $95K to $150K.
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.
RadNet AI Hiring
RadNet has 1 open AI role right now. They're hiring across Data Scientist. Based in MD, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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