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About This Role
Job Summary
We are seeking an expert‑level Data Scientist to lead state‑of‑the‑art research and develop advanced multimodal imaging models for our diagnostic intelligence platform. This role is designed for a senior practitioner with deep experience in medical imaging, radiology workflows, and machine learning research — someone capable of driving innovation from concept and academic rigor through to production‑grade deployment in clinical environments.
Responsibilities
- Lead ideation, research, and development of next‑generation models across CT, MRI, X‑ray, ultrasound, and multimodal fusion architectures.
- Architect and execute deep academic‑level research, including novel model designs, benchmarking, ablation studies, and scientific validation.
- Translate research outputs into production‑ready systems, ensuring reliability, scalability, and clinical relevance for real‑world diagnostic workflows.
- Build and oversee pipelines for dataset ingestion, cleaning, labeling, augmentation, multimodal alignment, and large‑scale imaging data management.
- Conduct expert‑level model evaluation, error analysis, robustness testing, and optimization for high‑performance inference.
- Ensure all workflows meet high regulatory standards, including data governance, auditability, reproducibility, and compliance expectations for medical AI.
- Collaborate closely with engineering, product, and clinical teams to deliver models that meaningfully advance diagnostic intelligence and clinical decision support.
Required Experience
- Master’s Degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Biomedical Engineering, or a closely related technical field.
- Expert proficiency in PyTorch, TensorFlow, Ultralytics YOLO, MONAI, and Hugging Face ecosystems.
- Demonstrated experience with medical imaging datasets, DICOM workflows, PACS systems, and radiology modalities.
- Hands‑on use of FiftyOne, Roboflow, Labelbox, V7, or Encord for dataset curation, annotation, and quality control.
- Proven ability to lead research initiatives and publish or present advanced imaging work at academic or industry venues.
- Strong background in segmentation, detection, multimodal architectures, and imaging‑specific preprocessing pipelines.
- Experience deploying models using Core ML, ONNX Runtime, NVIDIA TensorRT/DeepStream, or major cloud ML platforms (Azure, AWS, GCP).
- Solid engineering foundation in Python, GPU computing, Docker/Kubernetes, and scalable ML systems.
- Familiarity with regulatory frameworks relevant to medical AI, including data privacy, auditability, and reproducibility standards.
Preferred
- Prior collaboration with radiologists, imaging scientists, or clinical imaging teams.
- Experience building multimodal (vision + text) diagnostic or triage models.
- Experience with working in a start-up company and small teams to develop big ideas for a global impact.
Join us to be at the forefront of healthcare innovation by harnessing the power of artificial intelligence! Your expertise will directly impact patient care quality while advancing scientific discovery through sophisticated analytics and scalable AI models.
Pay: $180,000.00 - $250,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Health insurance
- Paid time off
- Retirement plan
Education:
- Master's (Required)
Experience:
- Machine learning: 5 years (Required)
- DICOM: 3 years (Required)
- AI: 5 years (Required)
- Python: 5 years (Required)
- Clinical information systems: 2 years (Preferred)
Work Location: Remote
Salary Context
This $180K-$250K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At American Radiologist Network, 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 $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($215K) sits 13% above the category median. Disclosed range: $180K to $250K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
American Radiologist Network AI Hiring
American Radiologist Network has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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