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About This Role
Become an everyday champion — and build a career where your impact fuels financial progress.
What We Do
CardWorks Financial Group is a diversified financial services platform building ethical solutions across credit, lending, and the full customer lifecycle. Through our family of companies, CardWorks Financial Group tackles the complex challenges that larger financial institutions leave behind. We’re embedded throughout the credit card ecosystem as a lender, servicer, and merchant acquirer.
Who We Are
- Merrick Bank: The bank that builds
- CardWorks Servicing: One partner, total performance
- Carson Smithfield: Resolution with respect
With nearly 40 years of operating history, our track record is solid: disciplined in downturns and built to accelerate in recovery. The CardWorks Financial Group companies take precise approach in complex markets, as a top three non\-prime focused general purpose card issuer and a top fifteen U.S. merchant acquirer.
Our team tackles the industry’s most complex credit and payment challenges. And we believe that excellent work starts with a team that feels supported, respected, and empowered to grow.
CardWorks Servicing, LLC provides end\-to end operational servicing functions for credit cards, secured cards, and installment loans. We service consumer and small business loans across the credit spectrum and offers backup servicing and due diligence services to capital providers and trustees.
Founded in 1997, Merrick Bank is an FDIC®\-insured financial institution headquartered in South Jordan, Utah, with over $10 billion in assets. A wholly owned subsidiary of CardWorks Financial Group, Merrick Bank serves roughly five million cardmembers and more than 100,000 merchant customers, offering credit cards, recreational loans, deposit accounts, merchant services and bank sponsorships to consumers and businesses.
Carson Smithfield, LLC provides a variety of post\-charge\-off debt recovery services, including digital self\-service, IVR, live agent, and external agency management.
Position Summary and Role Impact:
The Director, Data Science – Competitive Intelligence, AI Insights \& Strategic Analytics is a highly visible leadership role responsible for transforming internal, external, structured, and unstructured data into actionable business intelligence, competitive insights, and strategic recommendations. Sitting at the intersection of analytics, risk, strategy, technology, and market intelligence, this role leads the development of enterprise reporting, competitor benchmarking, AI\-driven intelligence solutions, and executive decision support capabilities across CardWorks Financial Group.
This position combines advanced analytics, machine learning, Generative AI, competitive intelligence, and industry research to identify growth opportunities, anticipate market movements, evaluate competitor performance, monitor emerging risks, and support strategic decision\-making. The role serves as a trusted advisor to executive leadership, business unit leaders, risk management, regulators, auditors, and board stakeholders by delivering data\-driven insights that influence business strategy, portfolio performance, and market positioning.
The successful candidate will champion an AI\-first and market\-first mindset, leveraging machine learning, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), and AI\-powered automation to accelerate intelligence gathering, uncover hidden opportunities, and create sustainable competitive advantage. This leader will also drive the build\-out of enterprise analytics capabilities, including KPI reporting, portfolio performance measurement, market intelligence platforms, competitor monitoring solutions, and executive AI copilots.
Essential Functions:
- Lead the development and delivery of enterprise reporting, executive dashboards, KPI scorecards, portfolio analytics, competitor benchmarking, and strategic business insights.
- Conduct in\-depth analysis of industry trends, competitive dynamics, emerging technologies, market opportunities, and regulatory developments affecting the consumer lending, credit card, banking, payments, and fintech industries.
- Apply advanced statistical techniques, machine learning models, and predictive analytics to identify patterns, forecast market changes, and support strategic business decisions.
- Design and implement AI\-powered market intelligence and competitive research solutions that automate the collection, synthesis, and interpretation of market data.
- Utilize Large Language Models (LLMs) to analyze and summarize earnings calls, SEC filings, analyst reports, press releases, industry publications, and competitor disclosures.
- Develop Retrieval\-Augmented Generation (RAG) solutions and AI agents that enable continuous monitoring of competitor activity, customer trends, market signals, and regulatory developments.
- Build executive AI copilots that support strategic planning, competitive analysis, and business decision\-making.
- Integrate external market, economic, customer, and industry datasets with internal business performance data to generate comprehensive insights.
- Analyze portfolio performance across lending products, identifying opportunities related to growth, profitability, customer behavior, risk management, and operational efficiency.
- Partner with business, product, marketing, digital, risk, technology, and finance leaders to translate analytical findings into actionable business recommendations.
- Support board presentations, regulatory requests, audit reviews, second\-line risk reviews, and executive strategic initiatives through rigorous analysis and communication.
- Prepare audit\-ready analyses and documentation to support regulatory, compliance, and governance requirements.
- Present complex analytical findings and strategic recommendations to executive leadership in a clear, concise, and compelling manner.
- Foster a culture of innovation, analytics excellence, data\-driven decision\-making, and AI adoption across the organization.
Requirements for Success:
Education \& Experience:
- Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, Data Science, Business Analytics, Financial Engineering, or related quantitative discipline required; Master's degree preferred.
- 5\+ years of experience in Business Analytics, Data Science, Competitive Intelligence, Market Research, Strategy Analytics, Risk Analytics, or related analytical roles.
- Experience within Financial Services, Consumer Lending, Credit Cards, Banking, Payments, or FinTech industries required.
- Strong understanding of lending economics, including interest income, interchange, fees, charge\-offs, delinquency, portfolio performance, and risk management frameworks.
- Proven experience applying machine learning, artificial intelligence, and advanced analytics to solve business problems and generate strategic insights.
- Experience working with Generative AI, LLMs, RAG architectures, AI agents, and market intelligence automation solutions preferred.
- Strong proficiency in SQL, Python, Snowflake, AWS, and modern analytics platforms.
- Experience with Power BI, PowerPoint, SAS, R, or similar analytical and visualization tools preferred.
- Demonstrated ability to communicate complex technical findings and strategic recommendations to executive leadership, regulators, auditors, and non\-technical stakeholders.
- Highly analytical self\-starter with the ability to manage multiple priorities and deliver results in a fast\-paced, highly regulated environment.
Leadership Competencies
- AI\-first and market\-first mindset.
- Strategic thinker with strong business acumen.
- Executive presence and storytelling capability.
- Strong stakeholder management and influencing skills.
- Curiosity\-driven approach to problem solving and innovation.
- Ability to challenge assumptions with data and evidence.
- Commitment to driving measurable business outcomes through analytics and intelligence.
Ideally, the qualified candidate will work at the following location(s): South Jordan, UT, Woodbury, NY, Wilmington, DE. A hybrid work model or fully remote model can be considered based on hiring manager decision and priorities of the role.
The salary range for this position, if located in NY Metro/NY State is $146,409 to $162,676\. However, please note that the salary range will vary for other geographic areas.
\#INDHP
Our Employee Value Proposition
- Competitive Pay, including a Bonus Target or Variable Pay Incentive Program
- Benefits Package \-Medical, Dental, and Vision (plus much more)
- 401(k) Plan with Company Match
- Short\- \& Long\-Term Disability
- Wellness Programs
- Group Life and AD\&D Insurance
- Paid Vacation, Sick Days and bank Holidays
- Employee Engagement Activities including Employee Appreciation Day, DEI Employee Resource Groups, Corporate Social Responsibility, Service Recognition
*We offer a total rewards package comprised of a competitive base rate of pay, variable pay incentive programs based on the role, and a comprehensive benefit suite. Offered rates of pay are determined based on job\-related knowledge, relevant experience, skills, certifications, and geographic location.*
*We are proud to be an equal opportunity employer. All qualified applicants will receive consideration without regard to age, race, color, sex, or gender identity/expression (including pregnancy, childbirth, transgender status, or sexual orientation), religion or creed, ancestry, citizenship, national origin, disability, military or veteran status, marital status, genetic information, or any other characteristic protected by applicable law.*
*We do not tolerate discrimination, harassment, or retaliation. Employment decisions are based solely on qualifications, merit, and business needs. Everyone is welcome here, and we hire based on your ability to do the job, not any protected characteristics.*
*If you need help or reasonable accommodation during the application or hiring process, please let your TA Partner know.*
Salary Context
This $146K-$162K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CardWorks Financial Group, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($154K) sits 29% below the category median. Disclosed range: $146K to $162K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CardWorks Financial Group AI Hiring
CardWorks Financial Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Woodbury, NY, US. Compensation range: $162K - $162K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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