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About This Role
Life Unlimited. At Smith\+Nephew we design and manufacture technology that takes the limits off living. The Director of Clinical Data \& AI is the global functional leader responsible for the strategy, architecture, and operational execution of clinical data and AI capabilities supporting end\-to\-end evidence generation. This role owns the clinical data lifecycle—from data acquisition and management to advanced analytics, AI enablement, and synthetic/simulated data—ensuring all data assets are high\-quality, interoperable, and fit\-for\-purpose for regulatory, scientific, and operational decision\-making. The Director serves as the enterprise authority on clinical data platforms and AI\-enabled evidence generation, driving integration across clinical systems, data engineering, AI/ML, and statistical/clinical programming. This position has full accountability for the strategy, execution, quality, and evolution of the Clinical Data \& AI function globally.
What will you be doing?
1\. Global Clinical Data \& AI Strategy
- Define and execute the global strategy for Clinical Data \& AI aligned to enterprise evidence\-generation and AI transformation goals
- Establish a unified operating model integrating:
+ Clinical systems (EDC, eCOA, registries)
+ Clinical Data Lake \& central data model
+ Data management and data engineering
+ AI/ML and advanced analytics
- Serve as the enterprise authority on clinical data architecture and AI enablement for clinical \& medical affairs across all BUs and geographies
- Partner with Clinical Study Management, Clinical Strategy, Regulatory, Medical Affairs, Statistics, and IT to define data\-driven evidence strategies
2\. Clinical Data Architecture \& Platforms
- Own the design, governance, and evolution of:
+ Clinical Data Lake (CDL) and standardized data models
+ Clinical systems ecosystem (EDC, eCOA, registry ingestion, integrations)
+ Data pipelines, transformation, and interoperability frameworks
- Ensure scalable, compliant, and extensible architecture supporting:
+ Cross\-study analytics
+ Real\-world data integration
+ Device \+ clinical data linkage
- Drive standardization (e.g., CDISC\-based models) and elimination of data silos
3\. AI, Data Science \& Advanced Analytics
- Lead development and deployment of AI/ML capabilities across the clinical lifecycle, including:
+ Data quality automation and monitoring
+ AI\-assisted clinical study reporting and analytics
+ Cross\-study insights and meta\-analyses
- Drive integration of AI into core workflows, not point solutions
- Establish best practices for:
+ Model development, validation, monitoring
+ Responsible AI (traceability, reproducibility, regulatory alignment)
- Oversee collaboration between data science, statistics, and programming teams
4\. Synthetic Data, Simulation \& Virtual Twins
- Own strategy and execution for:
+ Synthetic clinical data generation
+ Simulation frameworks for study design and operational planning
+ Virtual twin development for patient\- and study\-level modeling
- Ensure alignment with regulatory expectations for transparency and scientific validity
- Integrate synthetic and simulated data into:
+ Study design optimization
+ Evidence generation (e.g., hybrid designs, external controls)
5\. Clinical Data Management \& Quality
- Oversee global clinical data management function, ensuring:
+ High\-quality, consistent, and inspection\-ready data
+ Efficient study startup (eCRF design, database builds) and closeout
+ Risk\-based monitoring and analytics\-driven data review
- Embed AI, machine learning modeling, and automation into CDM workflows to improve efficiency and quality
- Ensure alignment with regulatory and compliance standards (FDA, EU MDR, GDPR, HIPAA)
6\. Statistical \& Clinical Programming Integration
- Own alignment and integration of:
+ Statistical programming (TFLs, ADaM outputs)
+ Clinical programming (data pipelines, transformations)
Ensure seamless data flow from raw data analysis\-ready datasets* reporting
- Drive standardization, automation, and reuse across studies and programs
- Leverage AI solutions to accelerate programming across Global Clinical and Medical Affairs
7\. Operational Excellence \& Delivery Model
- Own intake, prioritization, and delivery across:
+ Data platform initiatives
+ AI/ML programs
+ Study\-level data operations
- Implement scalable delivery models for standardized multi\-source clinical outcomes datasets from the Clinical Data Lake to key business stakeholder teams
- Optimize resourcing across:
+ High\-throughput standardized work
+ High\-complexity AI/data science initiatives
8\. Regulatory \& Data Governance Leadership
- Ensure all clinical data and AI activities are:
+ Compliant with global regulatory requirements
+ Traceable, auditable, and reproducible
- Establish strong governance across:
+ Data standards and lineage
+ AI model lifecycle
+ Data privacy and security
- Support regulatory submissions with robust, defensible data strategies
Key Interfaces
- Global Clinical Research Operations leadership
- Clinical / Medical Affairs / Regulatory Affairs
- Statistics, Data Science, and AI teams
- IT / Digital / Enterprise Data organizations
- External partners, CROs, AI vendors, and regulators
Education
- BA required, PhD (preferred) or Master’s in Data Science, Biostatistics, Computer Science, or related field
What will you need to be successful?
- Minimum of 10 years experience across clinical data, AI/ML, and data platforms in medtech/pharma/biotech
- Proven leadership of multi\-domain teams (data management, engineering, data science, AI, programming)
- Demonstrated ownership of enterprise data architecture (e.g., data lake/platform) – Databricks preferred
- Strong track record supporting regulatory submissions and clinical evidence generation
- Enterprise mindset – integrates data, AI, and operations into a unified capability
- Technical depth \+ breadth – credible across data engineering, CDM, AI, and analytics
- Regulatory credibility – understands how data and AI decisions impact submissions
- Execution rigor – delivers scalable, high\-quality platforms and outputs
- Transformational leadership – embeds AI into workflows, not as isolated innovation
- Pragmatic innovation – advances capabilities while maintaining compliance and reliability
You Unlimited.
- The anticipated base compensation range for this position is $165,250\-$236,000 USD annually. The actual base pay offered to the successful candidate will be based on multiple factors, including but not limited to job\-related knowledge/skills, experience, and geographic location. Compensation decisions are dependent upon the facts and circumstances of each position and candidate. In addition to base pay, we offer competitive bonus and benefits, including medical, dental, and vision coverage, 401(k), tuition reimbursement, medical leave programs, parental leave, generous PTO, paid company holidays, 8 hours of volunteer time annually, and a variety of wellness offerings such as EAP.
- Inclusion \+ Belonging: Committed to Welcoming, Celebrating and Thriving. Learn more about our Employee Inclusion Groups on our website https://www.smith\-nephew.com/
- Your Future: 401k Matching Program, 401k Plus Program, Discounted Stock Options, Tuition Reimbursement
- Work/Life Balance: Flexible Personal/Vacation Time Off, Paid Holidays, Flex Holidays, Paid Community Service Day
- Your Wellbeing: Medical, Dental, Vision, Health Savings Account (Employer Contribution of $500\+ annually), Employee Assistance Program, Parental Leave, Fertility and Adoption Assistance Program
- Flexibility: Hybrid Work Model (For most professional roles)
- Training: Hands\-On, Team\-Customized, Mentorship
- Extra Perks: Discounts on fitness clubs, travel and more!
Smith\+Nephew provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.
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Salary Context
This $165K-$236K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Smith+Nephew, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($200K) sits 8% below the category median. Disclosed range: $165K to $236K.
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.
Smith+Nephew AI Hiring
Smith+Nephew has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Andover, MA, US. Compensation range: $236K - $236K.
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 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 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).
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 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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