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About This Role
Manager, Data Science – Merchandising Category Analytics
Global Data & Analytics | Alimentation Couche-Tard
Location: Charlotte, NC
JOIN OUR TEAM! At Circle K, our mission is simple: to make our customers’ lives a little easier every day. You may have already stopped for coffee, refueling your car, or eating something on the go. Then, you know what Circle K is. Since the opening of a first store in Laval, Québec, our parent company, Alimentation Couche-Tard has never ceased to grow. We have grown into a successful global company with over 17,300 stores in 29 countries, serving almost 8.5 million customers every day. In total, more than 146,000 people work in our stores and offices. We make journeys easier by offering fast and friendly service. We care about our people and our communities, and we look for ways to uplift people first. Wherever your journey’s going, we can help you get there. Are you ready to grow your career? Let's grow together!
The Role
We are seeking an innovative, dynamic Data Science Manager to lead Data Science, Machine Learning, and AI initiatives for Merchandising Category Analytics. As a key leader within the Global Data & Analytics team, you will partner with Merchandising, Category Intelligence & Enablement (CIE), and Technology teams to translate strategic priorities into scalable, data-driven solutions that improve decisions and business performance.
The ideal candidate brings deep technical expertise, commercial acumen, product thinking, and strong people leadership. You will lead a high-performing team of data scientists, analysts, and machine learning engineers, taking solutions from opportunity identification through deployment, adoption, and measurable value realization.
What you’ll do
Strategy and Business Partnership* Shape the Data Science and AI portfolio: Develop and execute initiatives aligned with merchandising and enterprise priorities.
- Partner with business leaders: Work with Merchandising and CIE leaders to identify high-value opportunities, define business problems, and translate needs into analytics and AI solutions.
- Influence decisions: Present actionable insights and recommendations to senior leaders to shape business strategy and strengthen value realization.
Solution Delivery and Innovation* Lead solution development: Guide the design and deployment of scalable solutions across pricing, assortment, promotions, forecasting, experimentation, optimization, customer insights, and category performance.
- Advance AI and ML innovation: Evaluate and apply emerging capabilities, including generative AI and AI agents, to improve merchandising decisions and productivity.
- Ensure end-to-end delivery: Lead initiatives from data discovery and experimentation through production deployment, adoption, governance, and value realization.
- Scale capabilities: Establish reusable methodologies, frameworks, and MLOps practices that enable solutions to scale across categories and business areas.
Leadership, Governance, and Value* Measure business value: Define success metrics and demonstrate the measurable impact of Data Science, ML, and AI investments.
- Lead and develop talent: Coach and develop data scientists and analysts while fostering technical excellence, innovation, collaboration, and continuous learning.
- Collaborate across Data & Analytics and Technology: Partner with CIE, Data Engineering, Data Products, and Technology teams to deliver scalable, governed solutions aligned with enterprise standards.
- Champion responsible AI: Promote strong practices for model governance, explainability, data quality, privacy, and responsible AI adoption.
What you’ll need
Education and Experience* Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline; an advanced business degree is an asset.
- At least eight years of experience in Data Science, Machine Learning, Advanced Analytics, or a related field, including three or more years in leadership roles delivering large-scale initiatives.
Technical Expertise* Strong knowledge of exploratory and descriptive analytics, hypothesis testing, A/B testing, experiment design, and Agile development practices.
- Proficiency in Python, SQL, and R, with hands-on experience using cloud data platforms such as Azure, Snowflake, or Databricks.
- Working knowledge of data engineering workflows, including ETL, batch and real-time processing, model deployment, MLOps, and version control.
Leadership and Business Capabilities* Strong commercial mindset and demonstrated ability to translate data, analytics, and AI/ML insights into clear decisions, strategies, and business outcomes.
- Proven experience in talent acquisition, team development, coaching, and mentoring.
- Excellent communication and stakeholder management skills, with the ability to explain technical concepts to audiences at all levels and influence senior leaders across geographies.
- Demonstrated success collaborating with cross-functional partners, including Data Engineering, Architecture, and Data Cloud Platform teams.
- Ability to prioritize multiple initiatives, communicate timelines clearly, and drive work proactively to completion.
- Adaptability, curiosity, and a commitment to continuous learning across business and technology domains.
Success in This Role
Success will be measured by the ability to connect merchandising strategy with Data Science, ML, and AI capabilities; deliver scalable solutions with measurable business impact; and build a high-performing data science organization.
Circle K is an Equal Opportunity Employer. The Company complies with the Americans with Disabilities Act (the ADA) and all state and local disability laws. Applicants with disabilities may be entitled to a reasonable accommodation under the terms of the ADA and certain state or local laws as long as it does not impose an undue hardship on the Company. Please inform the Company’s Human Resources Representative if you need assistance completing any forms or to otherwise participate in the application process.
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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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Circle K, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Circle K AI Hiring
Circle K has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tempe, AZ, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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