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About This Role
We are looking for a Customer Marketing Data Scientist who combines strong technical expertise with the ability to own business problems end\-to\-end, partner with stakeholders, and drive decisions through data. In this role, you’ll work hands\-on with data while also acting as a key thought partner to Marketing and cross\-functional teams. Your success will be defined not just by the models you build, but by your ability to translate insights into action and influence strategy.
If you thrive in ambiguity, enjoy solving real business problems, and can clearly communicate data\-driven recommendations to non\-technical audiences, this could be your ideal fit.
Why join us:
Culture: Join a supportive and inclusive work environment where collaboration, respect, and open communication are at the core of everything we do.
Competitive Compensation: We offer highly competitive compensation and a total rewards package, ensuring that your hard work and dedication are recognized and rewarded accordingly.
Flexibility: We understand the importance of work\-life balance and offer various flexible schedules to help you manage your personal and professional commitments effectively.
Technology: Work with state\-of\-the\-art tools and technologies that empower you to excel in your role and stay at the forefront of industry trends.
Employee Assistance Programs: We care about you! You and your family will have access to LYRA, an industry leading platform that provides comprehensive support and a myriad of resources to help support your physical, mental, financial and social well\-being.
Opportunity: A continuous focus on professional development with many opportunities for training \& career growth.
Safety Focused: We care about you and have developed a 24/7 safety mindset that is showcased throughout every facet of the organization.
What you’ll do:
- End\-to\-End Analytics Ownership: Lead analytics initiatives from problem definition through to insight delivery and business adoption, ensuring outcomes; not just outputs.
- Business Collaboration \& Influence: Partner with Marketing, BI, IS, and other teams to understand challenges, proactively identify opportunities, and shape decisions through data.
- Data Storytelling \& Communication: Translate complex analysis into clear, actionable insights; present findings in a way that drives understanding and decision\-making across all levels.
- Customer \& Marketing Insights: Deliver analysis that informs segmentation, retention, campaign performance, and customer strategy.
- Test\-and\-Learn Approach: Design experiments, iterate quickly, and continuously improve impact through data\-driven learning.
- Technical Execution: Work hands\-on across the data lifecycle (data prep, modeling, analysis), applying advanced analytics and ML techniques to real business problems.
- Cross\-Functional Delivery: Collaborate effectively across a geographically dispersed team, ensuring alignment, transparency, and progress.
What you bring:
- Bachelor’s degree (or equivalent hands\-on experience) in Data Science, Statistics, Computer Science, Marketing, Social Sciences, or a related field; certifications in analytics or AI/ML are an asset
- 3\+ years of experience in marketing, customer analytics, or data science in a business\-facing role
- Proven ability to own analytics projects end\-to\-end; from problem definition through to insight adoption and measurable business impact
- Strong problem\-solving skills with the ability to translate ambiguous business questions into structured analytical approaches
- Proficiency in SAS, Python, R, and/or SQL, with experience working with large, complex datasets
- Experience working with cloud and data platforms such as Azure and Databricks
- Experience with data visualization and BI tools such as Power BI, Tableau, or Looker to communicate insights effectively
- Experience applying predictive modeling, segmentation, experimentation, and/or marketing analytics techniques
- Ability to connect technical outputs to clear business insights and strategic recommendations
- Strong communication and storytelling skills; able to present complex ideas in a clear, concise, and actionable way for non\-technical audiences
- Comfortable partnering with and influencing stakeholders, with a proactive, curious, and outcome\-driven mindset that balances hands\-on execution with big\-picture thinking
Why join us:
- Ability to connect technical work to business impact and strategy
- Strong communication skills—able to make complex insights accessible and actionable
- Comfortable engaging stakeholders, challenging assumptions, and guiding direction
- Curious, proactive, and focused on delivering measurable outcomes
Salary: $85,000 \- $105,000 annually (based on skills, experience, qualifications, and geography).
Eligible employees may earn performance\-based incentives and have access to comprehensive benefits and retirement plans with matching contributions.
This posting is a replacement for an existing vacancy. We do not use AI tools in the selection process and do not request pay history.
Here at Superior Plus Propane, we are an equal opportunity employer committed to the inclusion and accommodation of all individuals, we welcome all qualified candidates to apply. If you have an accommodation need during the recruitment \& selection process, we encourage you to connect with us at [email protected] to let us know how we can enhance your experience.
Salary Context
This $85K-$105K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Superior Plus Propane, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($95K) sits 47% below the category median. Disclosed range: $85K to $105K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Superior Plus Propane AI Hiring
Superior Plus Propane has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $105K - $105K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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