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About This Role
About Shamrock Trading Corporation
Shamrock Trading Corporation is a family of brands that has been serving the transportation and logistics industries since 1986\. Shamrock offers factoring solutions, export financing, third\-party logistics, a fuel card program and trucking technology.
Shamrock’s mission since the start has been to create value and success for our customers, our partners and our people. Strong ethics, dedication to our customers and close attention to the marketplace are critical to the success and growth of the Shamrock brands. Shamrock is headquartered in Overland Park, Kansas, with multiple offices throughout the U.S. Overland Park is a great place to live, work and play, being conveniently located within driving distance of everything Kansas City has to offer. Housed within the heart of Overland Park, our offices include 4 gorgeous towers on the East and West sides of Metcalf Ave. With a heavy community presence and a winning culture, Shamrock is a great place to work in Overland Park!
Why You’ll Love Working Here
Our award\-winning culture is a testament to our commitment to growth, recognition and empowerment. From day one, we prioritize employees first, encouraging them to think and act like owners and providing endless opportunities for growth and self\-development, both personally and professionally. Shamrock has been recognized as one of America’s 2025 “Most Loved Workplaces” by Newsweek.
At Shamrock, we hire bright, ambitious people and give them the tools they need for a successful, long\-term career here. Shamrock also offers a premier set of benefits for employees and their families:
- Training and Development: Ongoing training and professional development opportunities
- Medical: Fully paid healthcare, dental and vision premiums for employees and eligible dependents, and gym benefits
- Financial: Generous company 401(k) contributions and employee stock ownership after one year
- Work\-Life Balance: Competitive PTO and work from home opportunities after an introductory period
About the Role
Shamrock Trading Corporation is looking for a Data Scientist to join our team with a focus on fraud detection and risk assessment across our factoring, payments, lending, and freight marketplace businesses. Data Scientists partner closely with business stakeholders, analysts, and Machine Learning Engineers to explore data, build and evaluate models, and communicate findings that drive efficiency, enable risk management, and support strategic objectives.
This role will apply statistical, analytical, and machine learning techniques to help Shamrock better identify suspicious behavior, reduce financial loss, improve risk decisioning, and support faster, more confident customer funding decisions. The Data Scientist will work closely with Product, Credit, Risk, Operations, Data Services, Machine Learning Engineers, and business stakeholders to understand fraud patterns, build risk signals, evaluate models, and translate findings into practical business action.
This role is well\-suited for a data scientist or advanced analytics professional with experience in fraud, risk, credit, marketplace abuse, payments, lending, logistics, or identity verification who wants to apply those skills to complex real\-world problems in freight and financial services.
What You’ll Do
- Translate fraud, risk, and credit questions into analytical and modeling approaches
- Analyze invoice, funding, payment, carrier, debtor, customer, behavioral, and operational data to identify risk patterns and emerging fraud trends
- Develop and evaluate analytical and machine learning models to support fraud detection, risk scoring, anomaly detection, and early warning indicators
- Partner with Product, Credit, Risk, Audit, Operations, and business stakeholders to define risk signals, success metrics, decision thresholds, and model evaluation criteria
- Coordinate with Machine Learning team to transfer custom models into production deployments and platform integrations
- Partner with Data Analysts and BI teams to connect fraud and risk initiatives with reporting, monitoring, and business performance metrics
- Document assumptions, methodologies, data limitations, model performance, and findings to support transparent decision\-making
- Stay current on fraud patterns and risk approaches across factoring, freight, logistics, payments, lending, and marketplace businesses
What You’ll Bring
Required
- Bachelor’s degree in a quantitative field (Statistics, Data Science, Analytics, Computer Science, Applied Mathematics, Engineering), or equivalent practical experience
- Experience with relational databases and SQL
- Experience using statistical programming languages (Python, R, etc.)
- Knowledge of modern data platforms or cloud environments (Databricks, AWS, etc.)
- Experience using Git and collaborative development workflows
- Experience applying statistical or analytical techniques to real\-world business problems (regression, classification, distributions, optimization, etc.)
- Experience visualizing/presenting data for stakeholders (Power BI, Tableau, etc.)
- Strong analytical thinking, problem\-solving skills, and curiosity about business, fraud, and risk drivers
- A drive to learn new technologies, fraud patterns, risk techniques, and industry context
Preferred
- Experience in fraud analytics, risk analytics, credit risk, underwriting, payments fraud, marketplace fraud, identity verification, logistics, freight brokerage, factoring, asset\-based lending, SMB lending, commercial lending, or financial services
- Experience building or supporting fraud detection, anomaly detection, risk scoring, credit scoring, identity risk, or transaction monitoring models
- Experience working with adversarial risk problems where bad actors change behavior in response to controls
- Experience with entity resolution, graph analytics, network analysis, behavioral analytics, or relationship\-based risk detection
- Experience working with invoice, payment, bank account, carrier, debtor, customer, document, device, or transaction\-level data
- Experience partnering with Product, Engineering, Operations, Credit, Risk, or Compliance teams to move analytical work into business processes or production systems
Apply Today!
We’re so excited to connect with you! If you want to work with forward\-thinking people in an award\-winning culture, submit your application today.
\#LI\-MB1 \#LI\-Hybrid
Having trouble submitting your application? Contact us at Recruiting@shamrocktradingcorp.com for support.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Shamrock Trading Corporation, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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.
Shamrock Trading Corporation AI Hiring
Shamrock Trading Corporation has 1 open AI role right now. They're hiring across Data Scientist. Based in Overland Park, KS, US.
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 Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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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