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About This Role
BeOne continues to grow at a rapid pace with challenging and exciting opportunities for experienced professionals. When considering candidates, we look for scientific and business professionals who are highly motivated, collaborative, and most importantly, share our passionate interest in fighting cancer.
Role Overview
The Senior Director, Advanced Analytics and Data Science will be part of the Advanced Analytics Center of Excellence (CoE), sitting within Global Insights and Analytics, and will lead the strategy, development, and scaling of advanced analytics and AI\-enabled solutions that enable data\-driven decision\-making across Commercial, Medical Affairs, and Clinical Operations. This leader will translate complex data into actionable insights across the patient journey, healthcare provider engagement, scientific exchange, clinical trial acceleration, and omnichannel customer experiences, while serving as an AI leader who helps the organization responsibly identify, prioritize, and scale opportunities to improve performance and deliver meaningful value to patients, healthcare professionals, and stakeholders.
This is a highly collaborative leadership role that partners closely with Commercial, Medical Affairs, Clinical Operations, Marketing, Sales, Market Access, Medical Excellence, Clinical Development, IT, Data Engineering, Legal, Compliance, and external partners to build scalable analytics and AI capabilities, strengthen data and model governance, and embed insights into day\-to\-day decision\-making.
Key Responsibilities
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### Analytics Strategy and Capability Development
- Define and lead the advanced analytics capability roadmap, aligning analytics investments with priorities across Commercial, Medical Affairs, and Clinical Operations and measurable enterprise outcomes.
- Develop patient journey and evidence\-oriented analytics using longitudinal healthcare data, claims, prescription, specialty pharmacy, EHR, clinical trial, real\-world data, and other relevant data sources to identify key moments of intervention, access barriers, adherence challenges, scientific insights, and CT acceleration.
- Design and scale analytics solutions that support omnichannel engagement, customer segmentation, campaign measurement, field effectiveness, medical engagement, and CT acceleration.
- Lead the development and deployment of predictive models, machine learning capabilities, next\-best\-action solutions, and decision\-support tools that enable more personalized, evidence\-based, and effective engagement and operational planning.
- Shape and evangelize the organization’s AI strategy for analytics, including practical use cases for generative AI, agentic AI, natural language interfaces, intelligent automation, and decision intelligence across Commercial, Medical Affairs, and Clinical Operations.
### Cross\-Functional Leadership and Business Partnership
- Serve as a strategic partner to Commercial, Medical Affairs, and Clinical Operations leaders by translating business, scientific, and operational questions into analytical approaches, data requirements, and actionable recommendations.
- Bridge business, medical, clinical, and technical teams by converting analytics outputs into clear narratives, decision frameworks, and implementation plans for stakeholders at all levels.
- Partner with IT, Data Engineering, Legal, Privacy, Compliance, Medical, and Clinical Operations stakeholders to ensure analytics products, AI/ML models, and data practices meet applicable privacy, security, scientific, regulatory, and ethical expectations.
- Champion responsible AI practices by promoting transparency, model explainability, bias awareness, human oversight, fit\-for\-purpose validation, and appropriate governance for AI\-enabled decision support.
- Collaborate with internal teams and external vendors to deliver high\-quality analytics solutions efficiently and with strong adoption across the organization.
### Team Leadership and Organizational Enablement
- Serve as a thought leader in driving the creation, responsible adoption, and continuous evolution of advanced analytics, AI, and machine learning capabilities across Commercial, Medical Affairs, and Clinical Operations teams.
- Lead, mentor, and develop a high\-performing team of data scientists, analysts, and analytics partners, while managing external resources as needed.
- Establish practical operating models, standards, and best practices that improve analytics quality, scalability, and speed to insight.
- Promote a culture of data\-driven decision\-making by increasing analytics literacy, building stakeholder confidence, and embedding insights into commercial planning, medical strategy, evidence generation, and clinical operations execution.
- Continuously identify opportunities to modernize tools, platforms, processes, and AI\-enabled workflows that improve the impact, efficiency, and scalability of analytics delivery.
Education Required:
- Bachelor’s degree required in a quantitative field such as biostatistics, statistics, mathematics, economics, data science, computer science, or a related discipline; Advanced degree preferred
Qualifications:
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- Bachelor’s degree in a required field with 12 \+ years of overall experience and 10 \+ years of experience in advanced analytics, data science, commercial analytics, medical analytics, clinical operations analytics, business analytics, or a related function within the pharmaceutical, biotech, healthcare, or life sciences sector.
- Demonstrated experience translating complex data and analytics into strategic recommendations for senior business stakeholders.
- Strong understanding of healthcare, medical, clinical, and commercial data sources, such as claims, prescription, specialty pharmacy, EHR, real\-world data, clinical trial operations data, market access, and third\-party syndicated datasets.
- Experience leading analytics initiatives that support commercial strategy, omnichannel engagement, patient journey insights, medical engagement, evidence generation, clinical trial feasibility, enrollment, site performance, customer targeting, segmentation, measurement, or predictive modeling.
- Proven ability to lead cross\-functional initiatives and influence stakeholders across business, technology, data, legal, compliance, and vendor teams.
- Demonstrated ability to identify high\-value AI opportunities, guide stakeholders through AI\-enabled transformation, and translate emerging technologies into practical business, scientific, and operational applications.
- Bachelor’s degree in statistics, data science, economics, engineering, business analytics, computer science, or a related quantitative field, or equivalent practical experience.
### Preferred Qualifications:
- Advanced degree, such as a Master’s, MBA, or Ph.D., in a quantitative, scientific, technology, or business discipline.
- Experience with modern analytics, AI, and data platforms, such as cloud data warehouses, data science workbenches, business intelligence tools, SQL, Python, R, machine learning operations, generative AI tools, and AI/ML deployment approaches.
- Experience building or scaling analytics capabilities in a fast\-paced, growth\-oriented organization.
- Strong executive communication skills, including the ability to develop clear narratives, simplify complexity, and present insights to senior leadership.
- Familiarity with pharmaceutical data privacy, compliance, governance, responsible AI considerations, and the practical application of AI in regulated life sciences environments.
Supervisory Responsibilities:
- This position may manage internal analysts, external vendors, agency partners, or contractors and may provide project leadership or informal guidance to junior analysts; no direct people management is required unless otherwise determined by business need.
Travel:
- Up to 20% for team meetings, stakeholder workshops, or business reviews may be needed.
Global Competencies
When we exhibit our values of Patients First, Driving Excellence, Bold Ingenuity and Collaborative Spirit, through our twelve global competencies below, we help get more affordable medicines to more patients around the world.
- Fosters Teamwork
- Provides and Solicits Honest and Actionable Feedback
- Self\-Awareness
- Acts Inclusively
- Demonstrates Initiative
- Entrepreneurial Mindset
- Continuous Learning
- Embraces Change
- Results\-Oriented
- Analytical Thinking/Data Analysis
- Financial Excellence
- Communicates with Clarity
Salary Range: $209,600\.00 \- $279,600\.00 annually
BeOne is committed to fair and equitable compensation practices. Actual compensation packages are determined by several factors that are unique to each candidate, including but not limited to job\-related skills, depth of experience, certifications, relevant education or training, and specific work location. Packages may vary by location due to differences in the cost of labor. The recruiter can share more about the specific salary range for a preferred location during the hiring process. Please note that the listed range reflects the base salary or hourly range only. Non\-Commercial roles are eligible to participate in the annual bonus plan, and Commercial roles are eligible to participate in an incentive compensation plan. All Company employees have the opportunity to own shares of BeOne Medicines Ltd. stock because all employees are eligible for discretionary equity awards and to voluntarily participate in the Employee Stock Purchase Plan. The Company has a comprehensive benefits package that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness.
We are proud to be an equal opportunity employer. BeOne does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, disability, national origin, veteran status or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need. In order to ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Era Veterans’ Readjustment Assistance Act of 1974, Title I of the Americans with Disabilities Act of 1990, and any other applicable federal, state or local laws, applicants who require reasonable accommodation in the job application process may contact [email protected].
Salary Context
This $209K-$279K range is above the 75th percentile 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 BeOne Medicines, 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 ($244K) sits 12% above the category median. Disclosed range: $209K to $279K.
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.
BeOne Medicines AI Hiring
BeOne Medicines has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $279K - $279K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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