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About This Role
Overview:
Our team members are the heart of what makes us better.
At Hackensack Meridian *Health* we help our patients live better, healthier lives — and we help one another to succeed. With a culture rooted in connection and collaboration, our employees are team members. Here, competitive benefits are just the beginning. It’s also about how we support one another and how we show up for our community.
Together, we keep getting better \- advancing our mission to transform healthcare and serve as a leader of positive change.
The Information Technology (IT) Lead Data Scientist will join a collaborative team of extremely talented Analysts, Engineers, Designers, and Managers across the Hackensack Meridian Health (HMH) network. This role requires using data analysis, applied mathematics, machine learning (ML), and large language models to build next\-generation predictive solutions and artificial intelligence (AI) tools that enable clinicians and other end\-users within the organization to perform their work more efficiently and effectively. In this role, there is a strong emphasis on using the agile development methodology to rapidly iterate and deploy impactful solutions within the healthcare organization.
This position will require you to travel 1 full week onsite per quarter and 1 additional onsite day per month.
Responsibilities:
A day in the life of a Information Technology (IT) Lead Data Scientist at Hackensack Meridian Health includes:
- Work with HMH stakeholders to document and understand their objectives and ideate how AI/ML solutions might achieve those objectives.
- Perform exploratory data analysis to understand the data available related to a particular business objective and if that data lends itself to the creation of an AI/ML solution that might achieve that business objective.
- Optimizing Large Language Model (LLM) output with prompt engineering; building (RAG) pipelines.
- Build AI/ML models that attempt to address a business objective given the data available; explore the model space to understand the optimal model for the particular use case and what the performance characteristics are.
- Create repeatable, interpretable, and scalable models that can be seamlessly incorporated into analytic data products.
- Engineer features by using business acumen to find new ways to combine data sources.
- Write production\-quality pipeline code used to load data, execute the AI/ML model, and store the results.
- Perform analyses of AI/ML models and systems, such as fairness, bias, and equity audits; performance analyses; impact assessments; and interpretability reports.
- Develop and commit Python code in such a way that it works in harmony with the code and systems being developed by the team's data engineers, software engineers, and data analysts.
- Play a key role in communicating ML/AI methodology and impact to senior management to inform strategic decision\-making. Demonstrate strong communication and interpersonal skills that will be used in leading and motivating team members.
- Proactively communicate with stakeholders about areas of concern regarding changes in work schedules. Track and mentor individual team members about time and work management.
- Develop and implement ML/AI strategies that align with business objectives. Manage multiple projects. Establish thought leadership within the organization.
- Create a culture of collaboration, innovation, and continuous improvement within the team. Ensure overall team performance in the delivery of ML/AI solutions.
- Other duties and/or projects as assigned.
- Adheres to HMH Organizational competencies and standards of behavior.
Qualifications:
Education, Knowledge, Skills and Abilities Required:
- Bachelor's degree in STEM or another related/relevant field of study; and Master's degree in data science\-related area.
- Minimum 6\+ years of experience working in a data science role.
- Expert\-level Python development experience.
- Expert\-level SQL experience.
- Proficient in developing and interpreting complex statistical models.
- Deep expertise in at least one aspect or area of data acquisition, cleaning, and curation; EDA; or statistical analysis.
- Excellent understanding of most aspects of clinical data; Very proficient with querying and using clinical data; Deep expertise in at least one aspect or area of clinical data.
- Excellent understanding of a wide range of ML models. Able to improve model performance with complex feature engineering and hyperparameter tuning. Ability to design and implement complex machine learning pipelines. Deep expertise in at least one aspect of machine learning.
- Expertise in prompt engineering methodology, model fine\-tuning, and ability to create RAG pipelines.
- Can create custom monitoring solutions as needed. Deep expertise in at least one area of ML Ops.
- Work effectively with team members and technical and non\-technical stakeholders.
- Excellent written and verbal communication skills.
- Proficient computer skills including but not limited to Microsoft Office and Google Suite platforms.
Education, Knowledge, Skills and Abilities Preferred:
- PhD degree.
- Minimum of 6\+ years of data analysis experience in healthcare.
- Proficiency in Epic Clarity clinical data models.
- Experience with Google Cloud Platform (Big Query, VertexAI, etc).
- Experience with Git and GitHub
- Experience with Docker.
Licenses and Certifications Preferred:
- Epic Clarity Data Model.
- Epic Clarity Clinical Data Model.
- Google Machine Learning Engineer Certification.
If you feel that the above description speaks directly to your strengths and capabilities, then please apply today!
Starting Minimum Rate: Minimum rate of $183,664\.00 Annually Job Posting Disclosure: HMH is committed to pay equity and transparency for our team members. The posted rate of pay in this job posting is a reasonable good faith estimate of the minimum base pay for this role at the time of posting in accordance with the New Jersey Pay Transparency Act and does not reflect the full value of our market\-competitive total rewards package. The starting rate of pay is provided for informational purposes only and is not a guarantee of a specific offer. Posted hourly rates may be stated as an annual salary in the offer and posted annual salaries may be stated as an hourly rate in the offer, depending on the level and nature of the job duties and credentials of the candidate. The base compensation determined at the time of the offer may be different than the posted rate of pay based on a number of non\-discriminatory factors, including but not limited to: Labor Market Data: Compensation is benchmarked against market data to ensure competitiveness. Experience: Years of relevant work experience. Education and Certifications: Level of education attained, including specialized certifications, credentials, completed apprenticeship programs or advanced training. Skills: Demonstrated proficiency in relevant skills and competencies. Geographic Location: Cost of living and market rates for the specific location. Internal Equity: Compensation is determined in a manner consistent with compensation ranges for similar roles within the organization. Budget and Grant Funding: Departmental budgets and any grant funding associated with the job position may impact the pay that can be offered. Some jobs may also be eligible for performance\-based incentives, bonuses, or commissions not reflected in the starting rate. Certain positions may also be eligible for shift differentials for work performed on evening, night, or weekend shifts. In addition to our compensation for full\-time and part\-time (20\+ hours/week) job positions, HMH offers a comprehensive benefits package, including health, dental, vision, paid leave, tuition reimbursement, and retirement benefits.
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 Hackensack Meridian Health, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Hackensack Meridian Health AI Hiring
Hackensack Meridian Health has 1 open AI role right now. They're hiring across Data Scientist. Based in Nutley, NJ, US.
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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