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About This Role
Exceptional care starts with exceptional people.
For us, this is more than a job– it’s a calling. It’s waking up each day, looking for ways to make a difference with a compassionate, inclusive and talented team. Ochsner empowers you to discover your passion, reach your potential, and Pursue Your Purpose. Exceptional care starts with exceptional people and teams. Together we are leading the way.
This senior-level role is pivotal in driving healthcare innovation through cutting-edge data science and machine learning technologies. The Sr Data Scientist not only develops advanced algorthims but also plays a key role in strategic decision-making processes. This individual will lead projects that optimize healthcare outcomes and improve financial efficiency and will act as a mentor and technical leader within the data science team. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential duties.
This job description is a summary of the primary duties and responsibilities of the job and position. It is not intended to be a comprehensive or all-inclusive listing of duties and responsibilities. Contents are subject to change at the company's discretion.
Education
Required - Bachelor's Degree in Data Science, Computer Science, Mathematics, Statistics, Economics, or other related field.
Preferred - Master's Degree in Data Science, Computer Science, Mathematics, Statistics, Economics, or other related field.
Work Experience
Required -5 years Data science or Analytics in Healthcare
Preferred Qualifications
- Five or more years of professional experience in data science, applied machine learning, artificial intelligence, advanced analytics, healthcare analytics, or a related quantitative or technical role.
- Demonstrated hands-on experience developing, evaluating, deploying, monitoring, or supporting production AI or machine-learning solutions.
- Experience evaluating generative AI, large language model, or conversational AI systems using methods such as evaluation frameworks, scenario-based testing, automated or human review, prompt experimentation, and controlled testing of model or configuration changes.
- Strong proficiency in Python and SQL, including experience developing data pipelines, advanced analytics, machine-learning models, or AI-based solutions.
- Strong foundation in statistical modeling and experimental design, including the ability to establish performance metrics, analyze results, and determine whether changes produce meaningful improvements.
- Experience working with large-scale datasets and cloud-based AI or machine-learning platforms, with familiarity with modern development, deployment, MLOps, or LLMOps practices.
- Ability to assess complex technical systems, diagnose performance issues, and translate findings into practical and measurable improvements.
- Strong written and verbal communication skills, including the ability to collaborate acrosstechnical and operational teams and explain findings, risks, and recommendations toleadership.
Additional Preferred Qualifications
- Hands-on experience with voice agents, conversational AI, natural language processing, agentic AI, or other generative AI applications.
- Experience supporting conversational AI across development, evaluation, deployment, production monitoring, and continuous improvement.
- Experience with AI evaluation approaches such as rubric-based scoring, human evaluation, LLM-as-judge, A/B testing, canary releases, or similar methodologies.
- Experience analyzing voice-agent performance, including transcripts, audio signals, intent recognition, task completion, escalation, transcription quality, and latency.
- Experience with prompt development, retrieval-augmented generation, tool use, knowledge sources, and conversational workflows.
- Experience working in healthcare or another regulated environment, including familiarity with responsible AI, privacy, security, and the handling of PHI, PII, or other sensitive information.
- Experience with R or another statistical programming language.
- Experience providing technical leadership or mentoring technical professionals.
Knowledge Skills and Abilities (KSAs)
- Exceptional verbal and written communication skills, with the ability to translate complex data science concepts into strategic business initiatives and influence stakeholders.
- Strong interpersonal skills with demonstrated leadership and mentorship capabilities.
- Expertise in advanced analytical techniques, problem-solving, and model evaluation, in addition to strong story-telling and trobleshooting skills.
- Advanced proficiency in programming languages such as Python and SQL, as well as expertise in specialized machine learning frameworks including but not limited to TensorFlow or PyTorch.
- Ability to communicate and present technical concepts to executive-level and cross-departmental audiences, including creating dashboards to aid in explaining analytics.
- Expertise in leading-edge data visualization tools and techniques, in addition to a strong understanding of charts, dashboards, and reporting tools.
- Exceptional attention to detal.
- Ability to lead and influence key business initiatives through positive relationships, effective communication, and cross-team colloboration.
Job Duties
- Leads and strategically directs the design, deployment, and evaluation of computational algorithms and predictive models across multiple business units, while overseeing junior data scientists on these projects.
- Researches and implements cutting-edge maching learning techniques and statistical tests to drive innovation in healthcare analytics by staying updated with current academic and industry trends.
- Establishes best practices for team in data science and software development, including version control, testing, and containerization, ensuring deployable models and repeatable analyses across teams.
- Develops internal training programs and guidelines as the quantitative subject matter expert (SME), providing mentorship to junior data scientists and guiding the team in project/program design, statistical methodology, and model interpretation.
- Aligns data science goals with business objective, working directly with business partners in project scoping, timeline management, and documentation.
- Influences organizational strategies through regular engagement, communication, and high-level presentations to both internal and external stakeholders.
- Authors technical reports, statistical analysis plans (SAP), white papers, and peer-reviewed publications as needed.
- Oversees the entire data pipeline, from acquisition to analytics, ensuring data quality and governance standards, and guiding junior team members in these processes.
- Performs other related duties as assigned.
The above statements describe the general nature and level of work only. They are not an exhaustive list of all required responsibilities, duties, and skills. Other duties may be added, or this description amended at any time.
Remains knowledgeable on current federal, state and local laws, accreditation standards or regulatory agency requirements that apply to the assigned area of responsibility and ensures compliance with all such laws, regulations and standards.
This employer maintains and complies with its Compliance & Privacy Program and Standards of Conduct, including the immediate reporting of any known or suspected unethical or questionable behaviors or conduct; patient/employee safety, patient privacy, and/or other compliance-related concerns. The employer is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
Physical and Environmental Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Sedentary Work - Exerting up to 10 pounds of force occasionally (Occasionally: activity or condition exists up to 1/3 of the time) and/or a negligible amount of force frequently (Frequently: activity or condition exists from 1/3 to 2/3 of the time) to lift, carry, push, pull, or otherwise move objects. Sedentary work involves sitting most of the time but may involve walking or standing for brief periods of time. Jobs are sedentary if walking and standing are required only occasionally, and all other sedentary criteria are met.
Normal routine involves no exposure to blood, body fluid or tissue and as part of their employment, incumbents are not called upon to perform or assist in emergency care or first aid.
The incumbent has no occupational risk for exposure to communicable diseases.
Because the incumbent works within a healthcare setting, there may be occupational risk for exposure to hazardous medications or hazardous waste within the environment through receipt, transport, storage, preparation, dispensing, administration, cleaning and/or disposal of contaminated waste. The risk level of exposure may increase depending on the essential job duties of the role.
Are you ready to make a difference? Apply Today!
Ochsner Health does not consider an individual an applicant until they have formally applied to the open position on this careers website.
Please refer to the job description to determine whether the position you are interested in is remote or on-site.**Individuals who reside in and will work from the following areas are not eligible for remote work position: Colorado, California, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Vermont, Washington, and Washington D.C.
Ochsner Health endeavors to make our site accessible to all users. If you would like to contact us regarding the accessibility of our website, or if you need an accommodation to complete the application process, please contact our HR Employee Solution Center at 504-842-4748 (select option 1) or [email protected]*. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.*
Ochsner is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to any legally protected class, including protected veterans and individuals with disabilities.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ochsner Health, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Ochsner Health AI Hiring
Ochsner Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New Orleans, LA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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