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About This Role
Hybrid, New York City NY or Downtown Chicago, IL
We’re looking for Senior Manager, Finance AI Enablement to join our Finance Transformation team in either Amer Sports’ office in NYC or Chicago!
Amer Sports is a global collective of iconic sports and outdoor brands, powered by a community of people who share a passion for authenticity, impact, and excellence. We bring together leading brands, talented teams, and a culture built on collaboration. If you’re looking for an environment where you can make an impact and grow your career, we invite you to explore the opportunity below.
What you will be doing:
Finance is moving beyond isolated AI experiments toward a new model of work, where AI improves reporting, accelerates analysis, automates repeatable activities, and helps teams make faster, better-informed decisions. The Senior Manager, Finance AI Enablement will help lead that shift across Finance.
Reporting to the Senior Director, Digital Finance Transformation, you will sit at the intersection of Finance, AI, and change. You will translate business needs into practical AI-enabled solutions, provide the delivery discipline behind the Finance AI portfolio, and build the capabilities that help Finance teams use AI confidently and responsibly. The role combines hands-on problem solving with senior stakeholder engagement and offers broad exposure across brands, regions, and Finance functions.
This role is designed for someone who can quickly understand a Finance problem, identify the right digital approach, build or coordinate a practical solution, and help the team adopt it.
Specific responsibilities include:
Deliver Practical AI Solutions Across Finance
- Own a portfolio of fast-moving AI and digital enablement opportunities, including executive dashboards, reusable AI skills, targeted automations, and team-specific workflow solutions
- Partner directly with Finance leaders and process owners to identify pain points, define requirements, prototype solutions, gather feedback, and move successful concepts into sustained use
- Use approved enterprise AI and productivity tools, primarily Claude and Microsoft Copilot, to prototype and build solutions directly where appropriate, while coordinating with technical specialists when deeper development or integration is required
- Identify solutions that can be reused across brands, regions, or functions and convert local wins into scalable Finance capabilities
- Help teams measure adoption, time saved, decision quality, and other relevant indicators of impact
Drive Finance AI Portfolio Delivery
- Provide program and project management support across priority Finance AI initiatives, creating clear plans, milestones, owners, decisions, and escalation paths
- Coordinate stakeholders across Finance, IT, GBS, Legal, Information Security, Internal Audit, and external partners to keep initiatives aligned and moving
- Prepare concise, executive-ready portfolio updates and Steering Committee materials that surface progress, impact, risks, dependencies, and required decisions
- Support use case intake and prioritization, helping distinguish strategic development opportunities from faster enablement solutions
- Strengthen delivery standards, documentation, governance, and knowledge transfer without adding unnecessary process
Build AI Capability and Adoption
- Develop Finance-specific training, demonstrations, playbooks, prompt libraries, and reusable examples grounded in real Finance work
- Lead hands-on training sessions, workshops, office hours, and town halls for audiences ranging from AI beginners to advanced users
- Partner with Finance AI Champions and transformation leaders to share successful use cases, build local capability, and create momentum across the organization
- Stay current on emerging AI capabilities and translate what matters into practical implications and opportunities for Finance
- Promote responsible AI use, appropriate human oversight, and early engagement with risk and control stakeholders
What you need to succeed:
- 6+ years of experience in management consulting, finance transformation, digital strategy, analytics, technology enablement, or a related field, including experience leading complex, cross-functional initiatives
- An undergraduate degree in finance, business, technology, or a related field
- Demonstrated ability to lead ambiguous, cross-functional work from problem definition through delivery and adoption
- A strong track record of stakeholder and client management, including working with senior leaders and translating between business and technical teams
- Hands-on experience using Claude, Microsoft Copilot, or comparable enterprise AI tools to solve business problems, including building reusable workflows, analytical outputs, automations, or other practical solutions
- A self-directed learning mindset and evidence of quickly building expertise in emerging tools and methods
- Strong written and verbal communication skills, including executive-level storytelling, facilitation, and training
What we consider a plus:
- Experience across both Claude or Microsoft Copilot platforms
- A master's degree in finance, business, technology, or a related field
- Working knowledge of financial reporting, FP&A, accounting, or other Finance processes
- Experience in management consulting or a comparable client-service environment
What’s in it for you:
At Amer Sports, you’ll be part of a global community of people who care about what they do and how they do it. Across our teams and brands, we are driven by ambition, collaboration, and a shared purpose of elevating the world through sport.
Benefits & Compensation:
- Medical, dental and vision
- 401k with company match
- Life insurance
- Pre-tax transit discounts
- Paid maternity/paternity leave
- Smart casual dress
- Employee product benefits: Enjoy access to employee discounts on quality products from our iconic brands.
A reasonable estimate of the annual pay range is $160,000 - $180,000 at the time of this posting. Within the range, individual pay is determined by factors such as job-related skills, relevant experience, education, geographic location, and/or training. Please note that the range reflects the base pay only and does not include our competitive bonus program.
What to expect from the recruitment process
- CV screening: Every CV is reviewed by a human.
- Phone screening: A conversation with Amer Sports’ HR manager to discuss the role, our organization, and your expectations.
- Interviews: Usually 2–4 rounds, including interviews with the hiring manager and prospective team.
- Case studies or assessments: Your role may require practical tests, such as presentations or case studies.
- Feedback and decision: We aim to provide feedback throughout the process.
Interested?
Please submit your application via the application form. CVs sent by email will not be considered or processed.
If you have any questions or need clarification at any stage of the application process or during your journey with us, please don't hesitate to reach out to [email protected] (HR). Apply and join us in striving for excellence at each step!
#LI-JC1
First application date: 8 October 2026 Employment type: Full Time Amer Sports
Amer Sports is a global group of iconic sports and outdoor brands, including Arc’teryx, Salomon, Wilson, Peak Performance, and Atomic. Our brands are known for their detailed craftsmanship, unwavering authenticity, and premium market positioning. As creators of exceptional apparel, footwear, and equipment, we pride ourselves on cutting-edge innovation, performance, and designs that allow elite athletes and everyday consumers to perform their best.
With over 15,400 employees globally, Amer Sports’ purpose is to elevate the world through sport. Our vision is to be the best sporting goods company in the world. With corporate offices in Helsinki, Munich, Kraków, New York, and Shanghai, we have operations in 40 countries and our products are sold in 100+ countries. Amer Sports generated $6.6 billion of revenue in 2025. Amer Sports, Inc. shares are listed on the New York Stock Exchange.
For more information, visit www.amersports.com.
Youtube Instagram Location: New York City Amer Sports is an equal opportunity employer. Qualified applicants will be considered without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, gender expression, protected veteran status, disability, or any other legally protected characteristics.
Salary Context
This $160K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AMER SPORTS, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
AMER SPORTS AI Hiring
AMER SPORTS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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