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About This Role
Wayne, United States of America \| Full time \| Home\-based \| R1542005
This is an exciting opportunity to work as a Senior Consultant in IQVIA, one of the world's leading multi\-disciplinary and cross\-functional teams working with Real World patient data to help our client base answer their key business questions, make more informed decisions, and deliver results. Our team offerings leverage advanced analytics, machine learning algorithms, and technology platforms for a variety of healthcare applications including finding undiagnosed patients with rare conditions, predicting patient behavior, such as switching, and understanding a patient’s diagnosis and treatment journey through the health system.
As part of this team, you will be at the forefront of evolving the team’s offerings while assuming the following responsibilities:
Note: This role is ideal for professionals who enjoy solving business problems and working with clients. You will act as the liaison between clients and technical teams, helping define use cases, prioritize requirements, and translate complex analytical concepts into actionable business recommendations, rather than performing hands\-on data science or machine learning development.
Essential Functions
- Business Development Support:
o Research clients’ business problems and find potential solutions through our offerings, while supporting proposals and client pitches. Work closely with internal stakeholders from around the business to ensure the proposed solution is truly innovative and provides an exciting value proposition to clients.
- Project Manager \& Mentor:
o Oversee the design and delivery of advanced statistical/machine learning projects such as predictive modeling, patient journeys, HCP targeting, and HCP alerts. You will be responsible for multi\-disciplinary projects involving other consultants, data scientists, developers, and clinical experts to translate clients’ business needs into analytical problems and deliver the findings in clinically and commercially meaningful interpretations. Throughout the process, you will apply problem solving and critical thinking, intellectual engagement, project management, and maturity in communication.
o Serve as project manager and lead larger or more complex customer projects dependent on AIML solutions/offerings. This includes managing timelines, project staff, margins, issue resolution, and creating/reviewing and presenting relevant project materials (e.g., methodology documentation, PowerPoint presentations for client meetings, etc.)
o Manage, mentor, and teach junior staff directly on projects
o Establish strong credibility and build a relationship with the customer
- Team Support:
o Supports creation of project road maps required to deploy completed solutions; contributes to product improvements and growth through regular feedback to Product Owners
o Support the team by helping to scope and execute new offerings and establish best practices to share with the broader team
o Support and take ownership of various internal initiatives including: project automation and standardization, mentorship of new employees, recruitment, thought leadership, and team building.
Qualifications
- BA/BSc preferably related to life sciences, chemistry, economics, business, engineering, mathematics, statistics, or computer science
- 6\-8 years experience
- A postgraduate qualification in life sciences, statistics or a related discipline is desirable although not essential
Desired Skills
- Strong capability in managing analytics projects and priorities so that deadlines are met while retaining consistently high\-quality deliverables
- Interest in working on GenAI/agentic workstreams
- Experience in healthcare/life sciences with focus on strategic business cases for analytics services or products
- Experience analyzing and interpreting various types of patient level data preferred
- Basic ability to extract, transform, analyze data through SQL/python helpful, but not required
- A deep understanding of the application of statistics/machine learning in healthcare/life sciences and the pharmaceutical industry or excitement to upskill in these areas
- Ability to think through and effectively communicate analytical and clinical concepts and problems
- Experience managing multi\-disciplinary teams
- Experience developing credible relationships with senior level managers and executives
- Knowledge of key issues and current developments in the healthcare and life sciences industries
- Strong PowerPoint and Excel skills, ability to storyboard and translate technical concepts into clear, concise messaging for various customer audiences
IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com
IQVIA is proud to be 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, disability, status as a protected veteran, or any other status protected by applicable law. https://jobs.iqvia.com/eoe
IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.
The potential base pay range for this role, when annualized, is $111,400\.00 \- $278,500\.00\. The actual base pay offered may vary based on a number of factors including job\-related qualifications such as knowledge, skills, education, and experience; location; and/or schedule (full or part\-time). Dependent on the position offered, incentive plans, bonuses, and/or other forms of compensation may be offered, in addition to a range of health and welfare and/or other benefits.
Where you'll work
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### Home\-based
You’ll work remotely, with the flexibility to do your best work from home. Supported by collaborative tools and a global team, you’ll stay connected while enjoying the autonomy and balance that comes with a fully remote role.
Culture
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Culture at IQVIA is built on a shared belief: that when people are empowered with better data, smarter technology and deeper expertise, they can change what’s possible for patients. Across every team and every corner of the globe, you’ll find colleagues who genuinely care — about the mission and about each other. That’s what makes this a place where people tend to stay, grow and do the best work of their careers.
Salary Context
This $111K-$278K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At IQVIA, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($194K) sits 9% below the category median. Disclosed range: $111K to $278K.
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.
IQVIA AI Hiring
IQVIA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Parsippany-Troy Hills, NJ, US, Wayne, NJ, US. Compensation range: $278K - $350K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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).
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 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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