Interested in this Data Scientist role at Zimmer Biomet?
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About This Role
At Zimmer Biomet, we believe in pushing the boundaries of innovation and driving our mission forward. As a global medical technology leader for nearly 100 years, a patient’s mobility is enhanced by a Zimmer Biomet product or technology every 8 seconds.
As a Zimmer Biomet team member, you will share in our commitment to providing mobility and renewed life to people around the world. To support our talented team, we focus on development opportunities, robust employee resource groups (ERGs), a flexible working environment, location specific competitive total rewards, wellness incentives and a culture of recognition and performance awards. We are committed to creating an environment where every team member feels inspired, invested, cared for, valued, and have a strong sense of belonging.
What You Can Expect
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Drives advanced analytics and optimization across our supply chain and operational ecosystem. Bridging advanced machine learning, optimization, and simulation with real\-world operational execution, delivering measurable impact across inventory, service levels, working capital, and patient care.
How You'll Create Impact
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- Establishes reliable, analytics\-ready datasets across the end\-to\-end supply chain and clinical usage data; partners with data engineering teams to ensure scalable, governed data pipelines.
- Designs, builds, and deploys global Multi\-Echelon Inventory Optimization (MEIO) models that balance service levels, cost, and risk.
- Develops and operationalizes demand forecasting models that account for variability, seasonality, market dynamics, and clinical drivers.
- Applies optimization techniques (linear programming, mixed\-integer programming, heuristics) to inventory positioning and replenishment decisions.
- Analyzes usage patterns of consigned and loaned medical devices across sites and procedures; develops analytics to recommend when to shift between consignment and ownership models.
- Reduces excess inventory, free working capital, and improves asset utilization without impacting patient care.
- Builds risk and disruption models to assess exposure to demand volatility, supplier constraints, and geopolitical or regional risks.
- Leverages simulation and what\-if analysis to test inventory and supply strategies prior to deployment.
- Supports supply chain resilience planning and contingency strategies.
- Establishes feedback loops to continuously refine forecasts, optimization logic, and assumptions.
- Uses analytical insights to streamline workflows and automate replenishment and decision processes.
- Partners closely with supply chain planners, procurement, logistics, finance, and field teams.
- Translates analytical outputs into clear, actionable recommendations for non\-technical stakeholders; drives adoption of data\-driven decision making across operational teams.
- Defines and tracks KPIs such as inventory turns, service levels, stockout rates, cost savings, and working capital improvements.
- Quantifies impact on operational efficiency, financial performance, and patient outcomes; communicates results to executive and operational leadership.
What Makes You Stand Out
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- Background in Healthcare Management Consulting with experience advising on operations, supply chain, or performance improvement.
- Demonstrated ability to operate across industries and functional domains.
- Experience working in regulated healthcare or medical device environments strongly preferred.
- Proven track record of moving analytics from concept to operational execution.
- Experience deploying models into operational systems.
- Strong adherence to production\-grade development practices: Modular, maintainable code; Version control (Git); Unit testing and documentation
- Ability to clearly communicate complex insights to clinicians, operators, and executives.
- Experience in demand forecasting and predictive analytics across multi\-stage supply chains.
- Expert proficiency in Python (ML, optimization libraries, automation) and/or R (statistical modeling) plus strong SQL skills for extracting, transforming, and analyzing large datasets from ERP, WMS, and clinical systems.
- Optimization algorithms (linear programming, heuristics, simulation\-based optimization).
- Risk modeling for supply variability and disruption scenarios.
- Experience working in cloud environments (Snowflake) for scalable analytics \- familiarity with distributed data processing and cloud\-native analytics patterns.
- Proficiency in Power BI, Matplotlib, Seaborn, or similar tools.
Your Background
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- Minimum Qualification: Bachelor's Degree and 6 years of relevant experience, or Associate's Degree and 8 years of relevant experience, or High School Diploma or Equivalent and 10 years of relevant experience
Travel Expectations
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- Up to 15%
Expected Compensation \- $140,000\-$175,000 base salary.
EOE
Salary Context
This $140K-$175K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Zimmer Biomet, 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 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 18% below the category median. Disclosed range: $140K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Zimmer Biomet AI Hiring
Zimmer Biomet has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $175K - $270K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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