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About This Role
About the Role: Canon USA in Melville, NY is currently seeking a AI Operations \& Data Analyst (Analyst, Data Analytics). In this role, you will sit at the intersection of data engineering, business strategy, and AI validation, to ensure our machine learning models and automated workflows deliver measurable value. You will be expected to develop and implement advanced AI capabilities to become a company expert in data collection, consolidation and analysis of real\-world service operations data, specifically focusing on the Field Service to ensure machine learning models and automated workflows deliver actionable insights. This role will require some development of data visualization solutions and training on how to use those solutions so that key stakeholders can access information and drive improvements. This position is full time and offers a hybrid work schedule requiring you to be in the office Monday, Tuesday and Wednesday and an option to work from home the remainder of the week (unless a specific business need arises requiring in office attendance on other days). Note that work schedules and office reporting requirements may change from time to time based on business needs. Your Impact: \- Delivers standard reports and data visualization by required deadlines with consistent and reliable accuracy \- Develops and implements AI solutions to automate and create efficiencies of analyses of large confidential data files to meet company goals and objectives \- Audits and validates AI\-generated outputs (LLM summaries, automated customer updates, predictive models) to eliminate hallucinations and ensure operational accuracy \- Tracks real\-time reliability, bias mitigation, and accuracy metrics for deployed operational AI models \- Translates ambiguous business challenges into structured data workflows, automation pipelines, and measurable KPIs \- Designs intuitive Tableau/Power BI dashboards and translate complex AI insights into compelling narratives for executive stakeholders \- Creates data summaries and visualizations with the ability to present those results in various forms (presentations, interactive platforms, reporting, etc…) \- Partners with engineering teams to scope, test, and deploy high\-impact workflow automation About You: The Skills \& Expertise You Bring: \- Bachelor's degree in a relevant field or equivalent experience required, plus 3 to 5 years of related experience \- Education/Experience preferred in Business, Mathematics, Statistics, Analytics, Finance or similar discipline \- 3\+ years of experience in data analytics, operations analytics, or chain, or print management operations \- Familiarity with cloud platforms (AWS ecosystem) and LLM orchestration frameworks (e.g., LangChain) \- Background supporting large enterprise accounts or client\-facing delivery \- Analytic experience preferred. Preferred experience in product/solutions sales \- Significant knowledge of Microsoft Excel, including pivot tables, charts, graphs, and complex formulas \- Knowledge of Microsoft Access (or similar database skills) a plus \- Knowledge of Salesforce.com and Oracle a plus \- Knowledge of Power BI, Tableau, Domo, other reporting packages a plus \- Very detail oriented and consistently accurate \- Overtime as required, especially during monthly reporting for management meetings We are providing the anticipated salary range for this role: $69,300 \- $103,770 annually. Company Overview: About our Company \- p { font\-size: 18px; } Canon U.S.A., Inc., is a leading provider of consumer, business\-to\-business, and industrial digital imaging solutions to the United States and to Latin America and the Caribbean markets. With approximately $28\.5 billion in global revenue, its parent company, Canon Inc., as of 2024 has ranked in the top\-10 for U.S. patents granted for 41 consecutive years†. Canon U.S.A. is dedicated to its Kyosei philosophy of social and environmental responsibility. To learn more about Canon, visit us at www.usa.canon.com and connect with us on LinkedIn at https://www.linkedin.com/company/canonusa.
Who We Are
Where Talent Fosters Innovation.
Do you want your next professional experience to be filled with purpose and opportunity, world\-class team members, and impactful work? Driven by our mission of exceeding customer expectations with our technologies and enriching the lives of our local communities and staff, we are a phenomenal team working collaboratively toward common goals. Our employees have a strong work ethic, creativity, and a cooperative spirit. We believe in integrity, respect, empowerment, and making a difference in the communities we serve. There is a strong sense of pride in what we do individually and together as a team. Join us and discover what it means to work for a global digital imaging leader with an unparalleled reputation for quality and innovation.
What We Offer
You’ll be joining a leader in digital imaging and innovation with an immense opportunity to make an impact and create your own rewarding career. We demonstrate commitment to our employees by offering a full range of rewards, including competitive compensation and benefits.
And Even More Perks!
- Employee referral bonus
- Employee discounts
- “Dress for Your Day” attire program (casual is welcome, based on your job function)
- Volunteer opportunities to give back to our local community
- Swag! A Canon welcome kit and official merch you can’t get anywhere else
†Based on weekly patent counts issued by United States Patent and Trademark Office.
All referenced product names, and other marks, are trademarks of their respective owners.
Canon U.S.A., Inc. offers a competitive compensation package including medical, dental, vision, 401(k) Savings Plan, discretionary profit sharing, discretionary success sharing, educational assistance, recognition programs, vacation, and much more. A more comprehensive list of what we have to offer is available at https://www.usa.canon.com/about\-us/life\-at\-canon/benefits\-and\-compensation
We comply with all applicable federal, state and local laws, regulations, orders and mandates, including those we may be required to follow as a federal government contractor/subcontractor.
You must be legally authorized to work in the United States. The Company will not pursue or support visa sponsorship. All applicants must reside in the United States at the time of hire.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
If you are not reviewing this job posting on our Careers’ site https://www.usa.canon.com/about\-us/life\-at\-canon, we cannot guarantee the validity of this posting. For a list of our current postings, please visit us at https://www.usa.canon.com/about\-us/life\-at\-canon.
\#CUSA
Workstyle Description: Hybrid \- This position is full time and offers a hybrid work schedule requiring you to be in the office three days a week and an option to work from home the remainder of the week (unless a specific business need arises requiring in office attendance on other days). Note that work schedules and office reporting requirements may change from time to time based on business needs. Posting Tags: \#PM19 \#LI\-AV1 \#LI\-HYBRID
Salary Context
This $69K-$103K range is in the lower quartile 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 Canon, 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 ($86K) sits 60% below the category median. Disclosed range: $69K to $103K.
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.
Canon AI Hiring
Canon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Melville, NY, US. Compensation range: $103K - $103K.
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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