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About This Role
Job Description
This role will reside within the DHH US Market Engagement Team for Pharma. This team provided strategic thought\-partnership to US commercial stakeholders through understanding brand strategy, co\-creating DHH demand and associated business requirements aligned to business priorities. The US Market Engagement team Quarterbacks demand fulfillment and leads the activation of DHH products and services.
The Associate Director, US Market Engagement, Data Science \& Advanced Analytics, will be responsible for developing data\-driven insights to inform brand strategy and tactics in support of the pharma portfolio. This role will focus on applying advanced data science to deliver strategic non\-product DHH services to US Pharma Business unit such as HCP targeting/segmentation, call planning, campaign measurement \& effectiveness etc.
Key Responsibilities:
- Translate business needs and priorities to build analytical plans that answer business questions by providing actionable insights. Effectively communicating advanced analytics to stakeholders to inform business decisions.
- Communicate \& collaborate effectively with cross functional teams with competencies in market research, forecasting, commercial analytics, data science, customer engagement, data strategy, population health, competitive insights, marketing operations, medical \& clinical backgrounds.
- Analyze data across disparate databases/sources (including claims/EMR, DDD, and other sources) to develop insights that inform commercial business strategies.
- Stay current with industry trends and advancements in analytical methodologies to bring new ideas forward (including application of GenAI) that will enhance analytic capabilities of the organization.
- Contribute to innovative experiments, specifically to idea generation, idea incubation and/or experimentation, identifying tangible and measurable criteria to make meaningful improvements to the business processes and strategies.
- Derive actionable business insights from various data sources, develop clear data story, and communicate effectively to business stakeholders.
- Lead and mentor a team of data scientists and business analysts (on\-shore and off\-shore), providing guidance on best practices in data analytics, modeling and data visualization, through the scope of a project.
The individual in this role will be an independent contributor or lead a small team, supporting commercial insight and analytics needs for US marketing teams. The person will have demonstrated consistently strong leadership skills, an ability to work objectively and deal well with ambiguity and strong partnership skills. They will empower a growth mindset and inspire a culture of continual learning. This role will require interfacing and collaborating with many teams requiring strong communication skills.
The preferred candidate will have an entrepreneurial spirit, consultative and strategic mindset, experience across therapeutic lines. They should have a demonstrated record of producing actionable insights from analytics leveraging digital/technology (AI, agentic etc.). The person should be able to identify the data, analytics and research needs to define, enable, and inform commercial strategies of our brand and business leaders. The selected candidate should have a strong understanding of US data landscape of pharma commercial analytics capabilities, and technology, including application of GenAI to drive forward thinking effectiveness. The individual who will thrive in this role will be motivated by developing a deep appreciation of pharma secondary data sources and possess an ability to define and translate objectives and business questions into analytical problems that require advanced and scalable solutions that produce actionable insights.
Education Requirements:
- BS (or equivalent) in Data Science, Mathematics, Economics, Computer Science, Statistics, Decision Science, Marketing, Engineering, or Public Health is required.
- MBA, MS or PhD degree is preferred
Required Experience \& Skills:
- 5\+ years of relevant experience delivering complex analytical projects in the pharmaceutical, biotech, consulting, or healthcare industry.
- Knowledge in mining patient medical claims/EMR data as well as other healthcare datasets with a strategic mindset and proven record of being able to produce actionable business insights that drive positive commercial results.
- Self\-motivated, proactive mindset, and ability to work independently.
- Story\-telling and executive presence with senior leaders.
- Effective interpersonal skills, communication skills, and stakeholder management, including experience collaborating in a matrixed organization and ability to align stakeholders on data\-driven decisions.
- Strong business acumen and consultative skills, with proven ability to comprehend pharmaceutical business processes and commercial strategies in detail and translate these into detailed business standards/questions that power analytics.
- Effective organizational and project management skills to work effectively across the organization and manage a team of business analysts and data scientists efficiently and effectively.
- Innovative thinking with affinity toward experimentation and appropriate risk\-taking, including curiosity about how agentic AI and automation can enhance commercial analytics and insight generation.
- Awareness of the opportunities, limitations, and responsible use of AI (including agentic AI) in a regulated environment (e.g., data privacy, validation of AI\-generated outputs).
- Prior hands\-on experience working with healthcare data especially EMR/EHR, Claims Data (i.e. IQVIA APLD) and various data offerings.
- Ability to develop efficient programming code and software assets that provide advanced analytical functionality.
- Proven data science experience leveraging advanced statistical methods, Machine Learning/ Artificial Intelligence, Natural Language Processing (NLP) modeling and model evaluation is a plus.
- Knowledge of both machine learning and descriptive algorithms/modeling using such languages as Python, R studio, SAS, SQL etc.
- Understands the business context, challenge, limitation, constraints and goals to effectively match the capability of the statistical and mathematical and M/L approaches to the business, scientific and operational challenge
- Travel may be required
Preferred Experience and Skills:
- Preferred: Prior hands\-on experience working with healthcare data and leveraging advanced analytics and modeling techniques
- MBA, MS or PhD degree is a plus.
Required Skills:
Brand Strategy, Business Analysis, Business Processes, Business Strategies, Clinical Immunology, Collaborative Communications, Commercial Analytics, Commercial Strategies, Communication, Customer Engagement, Data Science, Demand Management, Innovation, Leadership, Marketing Data Analytics, Market Research, Natural Language Processing (NLP), Python (Programming Language), Requirements Management, Sourcing and Procurement, Stakeholder Relationship Management, Strategic Planning, Strategic Thinking
Preferred Skills:
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US and Puerto Rico Residents Only:
Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities. Please click here if you need an accommodation during the application or hiring process.
As an Equal Employment Opportunity Employer, we provide equal opportunities to all employees and applicants for employment and prohibit discrimination on the basis of race, color, age, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or other applicable legally protected characteristics. As a federal contractor, we comply with all affirmative action requirements for protected veterans and individuals with disabilities. For more information about personal rights under the U.S. Equal Opportunity Employment laws, visit:
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We are proud to be a company that embraces the value of bringing together, talented, and committed people with diverse experiences, perspectives, skills and backgrounds. The fastest way to breakthrough innovation is when people with diverse ideas, broad experiences, backgrounds, and skills come together in an inclusive environment. We encourage our colleagues to respectfully challenge one another’s thinking and approach problems collectively.
Learn more about your rights, including under California, Colorado and other US State Acts
The salary range for this role is
$156,900\.00 \- $247,000\.00
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs.
The successful candidate will be eligible for annual bonus and long\-term incentive, if applicable.
We offer a comprehensive package of benefits. Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid holidays, vacation, and compassionate and sick days. More information about benefits is available at https://jobs.merck.com/us/en/compensation\-and\-benefits.
You can apply for this role through https://jobs.merck.com/us/en (or via the Workday Jobs Hub if you are a current employee). The application deadline for this position is stated on this posting.
San Francisco Residents Only: We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance Ordinance
Los Angeles Residents Only: We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance
Search Firm Representatives Please Read Carefully
Merck \& Co., Inc., Rahway, NJ, USA, also known as Merck Sharp \& Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre\-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.
Employee Status:
Regular
Relocation:
No relocation
VISA Sponsorship:
No
Travel Requirements:
10%
Flexible Work Arrangements:
Hybrid
Shift:
Not Indicated
Valid Driving License:
No
Hazardous Material(s):
N/A
Job Posting End Date:
07/29/2026\*A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.
Requisition ID: R386296
Salary Context
This $156K-$247K 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 Merck, 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 ($201K) sits 8% below the category median. Disclosed range: $156K to $247K.
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.
Merck AI Hiring
Merck has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Rahway, NJ, US, Cambridge, MA, US. Compensation range: $203K - $272K.
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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