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About This Role
Discover Better Health Careers with Rendr!
Who We Are
Rendr is the leading primary care focused, multi\-specialty medical group dedicated to serving the Asian community in New York City. We strive to provide world\-class, value\-based health care with kindness at more than 100 clinical offices throughout Brooklyn, Manhattan, Queens, Staten Island, and Nassau County.
Why Join Rendr?
- Opportunities for professional growth and development.
- Competitive salary and benefits package. *(Salary is based on previous experience and years of service.)*
- Join a team that values employee, embraces diversity, and is committed to making a meaningful impact within our communities.
Benefits We Offer:
- Medical, Dental, and Vision Insurance
- 401k with Company Match
- Paid Time Off
- Paid Holidays/ Floating Holiday(s)
- Commuter Benefits
- Health Savings Account/ Flexible Spending Account/ Dependent Care Account
- Annual Performance Bonus
Job Overview:
The Compliance Data Scientist is responsible for developing, designing, producing, and Compliance interpreting complex data sets within the compliance department and implementing advanced analytical models to identify compliance risks, ensure regulatory adherence, detect and prevent fraud, waste, and abuse, and support risk\-based strategic decision\-making within the compliance department. The ideal candidate will have a strong background in healthcare compliance, data science, and statistical analysis, with a deep understanding of current Federal and New York State healthcare regulations and trends. This role requires expertise in transforming raw data into actionable insights through analysis, intuitive visualization, and effective communication.
Primary Responsibilities:
- Lead the development and implementation of advanced analytical models to identify and mitigate compliance risks in a timely manner.
- Collaborate closely with cross\-functional teams to extract, analyze, and validate data from various sources and prioritize business needs.
- Develop departmental dashboards, reports, and visualizations to monitor compliance metrics and trends.
- Provide insights and recommendations based on quantitative and qualitative data analysis.
- Support and ensure compliance with regulatory requirements through data\-driven strategies.
- Stay updated on industry trends and best practices in compliance analytics.
- Apply sophisticated data analysis techniques, including machine learning and predictive modeling, to identify and prevent fraud, waste, and abuse within the Medicaid program.
- Utilize detection methods such as pattern recognition, outlier analysis, and predictive modeling to proactively detect suspicious patterns and irregularities in data.
- Enhance the ability to apply analytics across multiple data sources including but not limited to the electronic medical record (EMR), Compliance, Billing, and Clinical.
- Develop advanced data analysis based on the compliance department workplan, utilize departmental incident management platform, and leverage advanced analytics and metric monitoring to identify trends and outliers.
- Conduct analyses to routinely and proactively detect suspicious patterns and irregularities across all data sets.
- Conduct outlier analysis by identifying data points that are impossible or significantly deviate from expected trends.
- Design and deliver reports and visual narratives that provide insights from analysis of data in line with Compliance initiatives.
- Create predictive models by developing machine\-learning algorithms to forecast trends.
- Continuously monitor analytics results and issues identified.
- Conduct trend analysis and provide data\-driven insights into recurring issues or policy violations.
- Analyze data from various internal and external sources to understand key information that can be used for proactive compliance risk identification, benchmarking, data visualization, and data reconciliation.
- Translate complex datasets into intuitive visual formats to support compliance presentations to the Board of Directors and other relevant stakeholders as needed.
Qualifications:
- Bachelor's degree in Data Science, Statistics, Computer Science, or a related field; Master's degree preferred.
- Minimum five (5\) years of healthcare\-related work experience with reporting and analytics, including data extraction, manipulation, statistical modeling, and data visualization.
- Proven experience in advanced analytics, data visualization, data modeling, and statistical analysis.
- Strong understanding of compliance regulations and risk management.
- Proficiency in analytical tools, machine learning, data visualization, data reconciliation, and data management including SQL and Microsoft Power BI.
- Excellent problem\-solving skills and attention to detail.
- Strong communication skills with the ability to present complex data insights to nontechnical stakeholders.
- Expert proficiency in Excel, including creating pivot tables.
- Ability to use various tools to create visualizations of analysis results.
- Familiarity with Federal and NYS Regulations.
Experience:
- Previous experience in compliance\-related roles, such as corporate compliance, the OIG, the NYS OMIG, Health plan SIUs.
- Strong understanding of industry\-specific compliance regulations and best practices.
- Certified in Healthcare Compliance (CHC), highly preferred.
- Experience working with electronic medical record (EMR) systems and healthcare data including patient records and claims data.
- Experience in designing and preparing reports for the Compliance Committee and/or Board of Directors.
- Experience and familiarity with Fraud, Waste, and Abuse (FWA), Centers for Medicare \& Medicaid Services (CMS), Drug Enforcement Agency (DEA), Food and Drug Administration (FDA), Office of Civil Rights (OCR) and Health Insurance Portability and Accountability Act (HIPAA)
Rendr is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
We look forward to reviewing your application and exploring the possibility of you joining our team!
Salary Context
This $110K-$135K range is in the lower quartile 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 Rendr, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($122K) sits 36% below the category median. Disclosed range: $110K to $135K.
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.
Rendr AI Hiring
Rendr has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $135K - $135K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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