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About This Role
### Manager, Data Science\- PVH Corp.
- R60492
- New York, United States
- Full Time
- PVH
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About Us:
We are brand builders who focus our passion and creativity to build Calvin Klein and TOMMY HILFIGER into the most desirable lifestyle brands in the world and at the same time position PVH as one of the best\-performing brand groups in our sector. Guided by our values and enabled by our scale and global reach, we are driving fashion forward for good, as one team with one vision and one plan. That’s the Power of Us, that’s the Power of PVH\+.
One of PVH’s greatest strengths is our people. Our collective desire is to create a workplace environment where every individual is valued, and every voice is heard, and we are committed to fostering an inclusive and diverse community of associates with a strong sense of belonging. Learn more about Inclusion \& Diversity at PVH here.
What You'll Do:
The Manager, Consumer Data \& Analytics drives measurable business impact by leading the design, development, and deployment of advanced analytical, statistical, and machine learning solutions. This role owns the modeling strategy, data science roadmap, and decision‑support frameworks that guide enterprise decision‑making across Commercial, Operations, and Consumer functions.
The Manager applies deep technical expertise — including predictive modeling, prescriptive analytics, optimization, statistical inference, data engineering, and large‑scale data processing — to translate complex business challenges into structured analytical approaches. This role ensures methodological rigor, builds scalable analytical pipelines, and delivers insights and recommendations that directly influence senior leadership decisions.
In addition, the Manager establishes modeling standards, governs analytical best practices, and provides technical leadership across project teams. The role mentors analysts, leads cross‑functional initiatives, and strengthens PVH’s enterprise data science capabilities through innovation, experimentation, and continuous improvement.
What You'll Do:
Strategic Ownership \& Decision Influence
- Own the end‑to‑end strategy for analytical modeling, data science frameworks, and decision‑support tools that drive measurable business outcomes.
- Define analytical approaches, select methodologies, and operate independently in ambiguous problem spaces.
- Deliver insights and recommendations that directly influence senior leadership decisions, including scenario modeling, ROI analysis, and performance optimization.
- Serve as a strategic thought partner to Commercial, Operations, and Executive leadership.
Advanced Modeling, Analytics \& Technical Execution
- Lead the design, development, and deployment of predictive, prescriptive, and optimization models across demand forecasting, inventory optimization, pricing, trade promotion, and consumer analytics.
- Apply advanced statistical and machine learning techniques.
- Build scalable data pipelines and modeling workflows using SQL, Python/R, Spark, and cloud‑based or distributed computing environments.
- Develop reusable code libraries, automated model training pipelines, and production‑ready analytical assets.
- Validate, test, and tune models to ensure accuracy, stability, and long‑term performance.
Governance, Standards \& Scalability
- Establish modeling standards, documentation practices, and methodological governance across the data science function.
- Ensure analytical solutions are scalable, repeatable, and aligned with enterprise data architecture.
- Drive continuous improvement of PVH’s analytical tools, platforms, and modeling infrastructure.
Cross‑Functional Leadership \& Collaboration
- Lead cross‑functional initiatives, aligning stakeholders across geographies and business units to ensure adoption of analytical solutions.
- Partner with business teams to define analytical requirements, KPIs, and success metrics.
- Translate complex analytical concepts into clear, compelling narratives tailored to diverse audiences.
- Interface with IT, Data Engineering, Marketing, Demand Leadership, and other partners to ensure alignment and execution.
Technical Leadership \& Mentorship
- Provide technical leadership to analysts and project teams, guiding modeling techniques, coding standards, and analytical best practices.
- Mentor junior team members and elevate the technical maturity of the broader organization.
- (If applicable) Manage an analyst, including coaching, prioritization, and performance development.
What You'll Bring:
Experience
- 4\+ years in an analytical or data science role within Consumer/Retail, with demonstrated ownership of modeling or decision‑support solutions.
- Strong experience with SQL and relational data modeling; ability to integrate data across disparate systems.
- Proficiency in Python and/or R for statistical modeling, machine learning, and data engineering.
- Experience with big data technologies (Hadoop, Spark) and distributed computing.
- Familiarity with BI tools (Power BI, Tableau) and scripting languages (VBA, DAX, PowerShell).
- Experience developing, maintaining, and integrating structured and unstructured datasets.
- Ability to communicate complex data in a simple, actionable way.
Education
- BS/BA in a quantitative field (Statistics, Applied Mathematics, Information Management, Engineering etc.) or equivalent experience.
- Master’s degree preferred.
Skills
- Advanced analytical toolkit: Excel, Looker, SQL, Python/R required.
- Strong technical depth across modeling, statistics, and data engineering.
- High attention to detail, strong ownership mindset, and entrepreneurial spirit.
- Excellent communication and storytelling skills.
- Ability to lead cross‑functional initiatives and influence at all levels.
- Strong organizational skills and ability to prioritize effectively.
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\#LI\-Hybrid
Pay Range:$104,500\-$141,200
PVH currently provides base salary ranges for all positions\-on job advertisements\-in the United States based on local requirements. These ranges are based on what PVH reasonably believes that it will pay an associate for their base salary for said position at the time of the posting. Individual compensation will ultimately be determined based on a variety of relevant factors including but\-not limited to qualifications, geographic location and other relevant skills. PVH is committed to providing a market\-competitive total rewards package to eligible associates, which includes diverse and robust health and insurance benefits to meet the varied needs of our associates and an above\-market 401(k) contribution to help our associates save for retirement. We also offer career growth opportunities, empowering our associates to design their future at PVH.
Additional Compensation: This role is bonus eligible.Your Wellbeing is Our Priority
At PVH, we offer competitive, cost\-effective, and comprehensive benefit packages. We strive to provide options when it comes to your health, finances, and work\-life balance. This includes:
- Pay \& Insurance: Competitive pay, bonus programs, best in class medical insurance, vision insurance, dental insurance, life insurance, disability insurance, and more.
- 401(k): An above\-market 401(k) contribution to help our eligible associates save for retirement.
- Flexible Workplace: Generous company\-paid holidays, paid time off, hybrid working arrangements, volunteer opportunities, seasonal hours, and flexible work schedules.
- Wellbeing Support: A variety of wellbeing tools and programs such as, Headspace membership, reimbursement for fitness memberships and/or digital meditation subscriptions, and the opportunity to earn up to $200 a year in rewards for exercising and participating in healthy activities.
- Care.com Services: Access to services for childcare, elder care, adoption preparation, pet care, plus reimbursement for backup care when your regular plans fall through.
- Education Assistance: Receive support for continued education including tuition reimbursement.
- Associate Discount: Shop at our company outlets and e\-commerce sites at a discount.
Please note, eligibility depends on employment status, location and length of time employed with PVH and our benefits may be subject to change. Applications will be accepted on a rolling basis until the position is filled.
*PVH Corp. or its subsidiary ("PVH") is an equal opportunity employer and considers all applicants for employment on the basis of their individual capabilities and qualifications without regard to race, ethnicity, color, sex, gender identity or expression, age, religion, national origin, citizenship status, sexual orientation, genetic information, physical or mental disability, military status or any other characteristic protected under federal, state or local law. In addition to complying with all applicable laws, PVH is also committed to ensuring that all current and future PVH associates are compensated solely on job\-related factors such as skill, ability, educational background, work quality, experience and potential.*
*To achieve these goals, across the United States and its territories, PVH prohibits any PVH employee, agent or representative from requesting or otherwise considering any job applicant’s current or prior wages, salary or other compensation information in connection with the hiring process. Accordingly, applicants are asked not to disclose this salary history information to PVH.*
Salary Context
This $104K-$141K range is in the lower quartile 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 PVH Corp., 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($122K) sits 44% below the category median. Disclosed range: $104K to $141K.
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.
PVH Corp. AI Hiring
PVH Corp. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $141K - $141K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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