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About This Role
Job Summary
The Finance Systems \& AI Enablement Manager maintains the technical infrastructure supporting KEEN's global Financial Planning \& Analysis (FP\&A) function. This includes supporting financial systems, preparation of global consolidated reporting, and developing AI\-driven workflows across the Finance department. The position ensures that the systems, data, and consolidated reports used by the FP\&A team and executive leadership are accurate, timely, and progressively automated. This role will serve as an advanced resource for the new AI Center of Excellence.
Essential Functions
Description
Financial Systems \& Reporting
- Serve as an advanced finance systems resource for KEEN's core financial planning, reporting, and consolidation systems, including SAP, Adaptive Analytics, and related FP\&A data flows.
- Support system configuration, user provisioning, security access, metadata updates, release testing, and ongoing system enhancements in partnership with Finance, Accounting, and GBT stakeholders.
- Maintain and update financial system structures, including chart of accounts, organizational hierarchies, cost centers, reporting dimensions, allocation rules, and integration mappings between SAP and the planning platform.
- Provide functional support to global finance users across regions by troubleshooting data, calculation, reporting, and access issues during close, forecast, budget, and Long Range Plan cycles.
- Support the SAP S/4HANA implementation and cutover for finance planning and reporting processes by gathering requirements, developing test scenarios, validating data during parallel runs, and assisting with post\-go\-live stabilization.
- Document system configuration, data flows, standard operating procedures, and process changes to support consistent system usage and reporting practices.
Consolidation \& Financial Reporting
- Lead the preparation of monthly, quarterly, and annual consolidated financial reporting across KEEN regions, channels, and reporting entities.
- Execute and review multi\-currency consolidation activities, including foreign exchange translation, constant\-currency reporting, intercompany eliminations, and reconciliation between local\-currency and U.S. dollar reporting views.
- Prepare consolidated reporting packages for actuals close, forecasts, budget, Long Range Plan, and group reporting using standardized reporting and variance analysis views.
- Analyze and validate consolidated revenue, gross margin, OPEX, Operating Income, and non\-financial KPI reporting to support accuracy, consistency, and timely decision\-making.
- Partner with Accounting, Treasury, regional finance teams, and other stakeholders to resolve reporting issues, improve data quality, and support timely close and planning processes.
- Apply KEEN's KPI definitions, reporting hierarchies, and consolidation standards consistently across recurring financial reporting deliverables.
- Identify and recommend process improvements to enhance reporting accuracy, efficiency, and consistency across consolidation and financial reporting activities.
AI Enablement
- Support Finance's AI enablement efforts by identifying, testing, and documenting AI use cases across FP\&A, consolidation, and reporting workflows.
- Maintain FP\&A AI reference materials, including the KPI Dictionary, Operating Manual, Company Primer, and Stakeholder Map, ensuring content remains accurate, current, and usable for finance users and AI\-enabled workflows.
- Develop and maintain AI\-assisted workflows for recurring finance deliverables, including variance commentary, monthly close narratives, quarterly business review materials, Board reporting, and Fuerst Group reporting.
- Evaluate and pilot AI tools and solutions that may improve finance processes, reduce manual effort, and support compatibility with future SAP environments.
- Apply established standards and guardrails for responsible AI use in Finance, including data handling, audit trails, human\-in\-the\-loop review, and accuracy validation.
- Train and support finance users on AI\-enabled processes to improve adoption, consistency, and AI literacy across the Finance function.
- Partner with the Senior Director, GBT, FP\&A, and Finance leadership to support continuous improvement of the planning, reporting, and consolidation calendar, controls, and documentation.
*Please note this job description is not designed to contain a comprehensive listing of activities, duties, orresponsibilities that are required of the employee for this job. Duties responsibilities and activities maychange at any time with or without cause.*
Qualifications
- Bachelor's Degree in Finance, Accounting, Information Systems, Economics, or a related discipline required.
- Master's Degree in Business Administration or relevant professional certification (CPA, CMA, CFA) preferred.
- Minimum three (3\) to five (5\) years of combined experience in FP\&A, financial systems administration, and/or global consolidated reporting, with demonstrated hands\-on system ownership.
- Any direct experience administering an enterprise planning platform (e.g., Workday Adaptive Planning / Adaptive Analytics, Anaplan, Oracle EPM, SAP Analytics Cloud) and an ERP (SAP required).
- Three (3\) years experience with global multi\-currency consolidation, FX translation, and intercompany eliminations.
- Demonstrated experience deploying AI, automation, or RPA tooling in a finance context required.
*Any equivalent combination of experience and education which clearly indicates the ability to perform the essential functions of the position may substitute on a year for year basis.*
Knowledge, Skills, and Abilities
- Strong hands\-on experience supporting ERP and planning systems, including metadata configuration, calculations, integrations, security, and troubleshooting.
- Advanced working knowledge of global financial consolidation, including FX translation, intercompany eliminations, constant\-currency reporting, and multi\-entity reporting hierarchies.
- Working knowledge of FP\&A processes, including budgeting, forecasting, long\-range planning, variance analysis, and management reporting.
- Ability to translate finance process requirements into effective system configurations, reporting structures, and data flows.
- Practical experience using AI tools in a business context, including LLM\-based assistants, workflow automation, and emerging AI\-enabled productivity tools.
- Ability to develop, prompt, test, and evaluate AI\-enabled workflows for finance use cases, applying established standards and review practices.
- Strong data discipline, including data integrity review, cross\-system reconciliation, data lineage documentation, and audit\-ready recordkeeping.
- Ability to analyze and explain financial, system, and data concepts clearly to finance and non\- finance audiences.
- Strong attention to detail, organization, and time management across overlapping close, forecast, planning, and project cycles.
- Strong written and verbal communication skills, with the ability to interact effectively with cross\- functional partners and management.
- Proficiency with SAP and Adaptive Analytics required; experience with Snowflake, Power BI, or similar BI/data platforms preferred.
- Advanced Microsoft Excel skills; proficiency with Microsoft Office Suite, including PowerPoint.
- Ability to learn quickly and adapt to changing systems, processes, and business needs, including ERP migration activity.
Travel Required: Yes, up to 10% of the time
Base Salary: $119,000 \- $125,600
*This range represents the low and high ends of this position's anticipated base salary range. The actual base salary will depend on numerous factors such as experience, knowledge, skills, and location. Our base salary is just one component of our competitive total rewards strategy, which includes numerous benefits and perks as well as specific health and welfare benefits.*
Why Work at KEEN: Driven by a passion for life outside, KEEN is a values\-led, independently owned brand from Portland, Oregon, that's on a mission to create original and versatile products, improve lives, and inspire outside adventure. Founded in 2003, KEEN launched a revolution in the footwear industry with the introduction of the Newport adventure sandal and has donated more than $18 million to non\-profit organizations and causes around the world to promote responsible outdoor recreation, including conservation efforts to protect open spaces. KEEN strives to show the world through its products and its actions that a business for good can actually be good for business. By giving back, reducing impact, and activating communities and individuals to protectthe places where we work and play, KEEN puts its values in motion and takes action to leave the world a better place.
*Fuerst Group, KEEN, and Chrome are equal opportunity employers. We value an inclusive and diverse community. Qualified candidates of all backgrounds are encouraged to apply and will be considered without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.*
Salary Context
This $119K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Keen Footwear, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($122K) sits 32% below the category median. Disclosed range: $119K to $125K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Keen Footwear AI Hiring
Keen Footwear has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Portland, OR, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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