Senior Data Scientist

$108K - $153K Irvine, CA, US Senior Data Scientist

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Skills & Technologies

JaxMlflowPower BiPythonPytorchTableauTensorflowTransformers

About This Role

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Many structural heart patients suffer from heart failure with limited options. Our Implantable Heart Failure Management (IHFM) team, part of the AI, Product and Platforms organization, is at the forefront of addressing these unmet patient needs through pioneering technology that enables early, targeted therapeutic intervention. Our innovative solutions are not just transforming patient care but also creating a unique and exciting environment for our team members. It is our driving force to help patients live longer and healthier lives. Join us and be part of our inspiring journey.

At Edwards Lifesciences, the Implantable Heart Failure Management (IHFM) AI, Product and Platforms organization designs and builds the software and data products that clinicians and patients depend on. As a Senior Data Scientist, you own modeling for a clinical or product domain, drive problem framing with product and clinical partners, own offline\-validation rigor, and author model documentation that meets FDA submission expectations for regulated products.

Based in Irvine, CA, you'll join a high\-impact medtech innovation hub in the heart of Orange County, collaborating in person with cross\-functional teams to shape patient\-focused technology.

How you'll make an impact

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  • Domain modeling. Own modeling for a clinical or product domain, for example, arrhythmia classification, cardiac imaging segmentation, clinical prediction, or patient outcome forecasting, from framing through offline validation.
  • Architecture selection. Select and adapt architectures for the modality (vision transformers or encoder\-decoder networks for imaging, transformers, or temporal models for signals) and apply self\-supervised and representation learning under limited labels.
  • Statistical depth. Apply survival analysis, Bayesian modeling, and causal inference where the clinical question requires (statsmodels, lifelines or scikit\-survival, PyMC).
  • Evaluation \& subgroup. Design offline evaluation, calibration, and clinical performance validation strategies, including subgroup and bias analysis across sex, age, and device cohort, with Medical Affairs and Clinical Science.
  • Interpretability \& documentation. Author submission\-grade model documentation including calibration, uncertainty, and interpretability evidence (SHAP, Captum), and improve the team's modeling and validation practice.
  • Research adoption. Evaluate and adapt current AI/ML for health research to IHFM problems, and mentor less experienced data scientists.
  • Handoff. Coordinate the handoff of offline\-validated custom models to AI/ML Engineers (Applied), and partner with Regulatory Affairs on submission strategy.
  • Algorithm development, analysis \& reporting. Create, test, and improve complex algorithms, NLP, and machine learning models; analyze results; develop insights; and produce reports and dashboards to communicate performance and findings to stakeholders.
  • Data preparation \& quality. Process, cleanse, label, and verify structured and unstructured data used for analysis; collaborate with internal teams and external partners on data standards, metrics, analytics, and reporting.
  • Tools, integration \& design controls. Identify and integrate data sources, software, and analytics tools (e.g., Python, SQL, SAS, Power BI, Tableau Prep); support development of design control documentation including algorithm requirements and risk documentation.

What you'll need (Required):

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  • Bachelor's in related field (e.g., Computer Science, Engineering, Biostatistics or Scientific) plus 4 years \-or\- Master's plus 3 years of previous experience including industry or industry/ education
  • Relocation is not provided for this role. Only candidates within a 50\-mile radius of Irvine, California will be considered.

What else we look for (Preferred):

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  • Depth in at least one data modality relevant to IHFM, for example, time\-series, or medical imaging (echocardiography, cardiac magnetic resonance imaging, or computed tomography).
  • Command of the relevant architecture families and of transfer and self\-supervised learning.
  • Strong Python and SQL, and depth in modeling and experiment tracking such as PyTorch, TensorFlow, and MLflow.
  • Command of evaluation, calibration, and clinical performance methodology, including subgroup analysis, and the ability to author documentation suitable for regulatory submission.
  • A track record of owning modeling for a domain and communicating results to clinical, product, and regulatory partners.
  • Experience with clinical validation, retrospective clinical data, or regulated medical software (SaMD).
  • Depth in time\-series or medical imaging modeling, including segmentation and registration, for example, MONAI, pydicom, or SimpleITK.
  • Bayesian modeling, causal inference (DoWhy, EconML), or survival analysis (lifelines, scikit\-survival).
  • Generative approaches for augmentation or synthetic data (autoencoders, GANs, or diffusion models).
  • Exposure to multimodal modeling, combining signals, imaging, labs, and notes into patient\-state models.
  • Familiarity with R, JAX, or hyperparameter optimization (Optuna, Ray Tune).
  • Contributions to internal or external research (publications, patents, or venues such as MICCAI or ML4H).

Aligning our overall business objectives with performance, we offer competitive salaries, performance\-based incentives, and a wide variety of benefits programs to address the diverse individual needs of our employees and their families.

For California (CA), the base pay range for this position is $108,000 to $153,000 (highly experienced).

The pay for the successful candidate will depend on various factors (e.g., qualifications, education, prior experience). Applications will be accepted while this position is posted on our Careers website.

E dwards is an Equal Opportunity/Affirmative Action employer including protected Veterans and individuals with disabilities.

COVID Vaccination Requirement

Edwards is committed to protecting our vulnerable patients and the healthcare providers who are treating them. As such, all patient\-facing and in\-hospital positions require COVID\-19 vaccination. If hired into a covered role, as a condition of employment, you will be required to submit proof that you have been vaccinated for COVID\-19, unless you request and are granted a medical or religious accommodation for exemption from the vaccination requirement. This vaccination requirement does not apply in locations where it is prohibited by law to impose vaccination.

Salary Context

This $108K-$153K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).

View full Data Scientist salary data →

Role Details

Title Senior Data Scientist
Location Irvine, CA, US
Category Data Scientist
Experience Senior
Salary $108K - $153K
Remote No

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 Edwards Lifesciences, 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

Jax (2% of roles) Mlflow (4% of roles) Power Bi (5% of roles) Python (51% of roles) Pytorch (15% of roles) Tableau (4% of roles) Tensorflow (11% of roles) Transformers (2% of roles)

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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($130K) sits 32% below the category median. Disclosed range: $108K to $153K.

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.

Edwards Lifesciences AI Hiring

Edwards Lifesciences has 1 open AI role right now. They're hiring across Data Scientist. Based in Irvine, CA, US. Compensation range: $153K - $153K.

Location Context

Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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

Based on 463 roles with disclosed compensation, the median salary for Data Scientist positions is $192,890. Actual compensation varies by seniority, location, and company stage.
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.
About 14% of the 3,708 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Edwards Lifesciences is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from Data Scientist positions include Senior Data Scientist, ML Engineer, AI Product Manager. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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