TreviPay
Sr Decision Science Analyst
- Location
- Overland Park, KS, US
- Arrangement
- Remote
- Employment type
- Other
- Level
- Senior
- Posted
- 28 August 2026 (about a month ago)
Checked 28 days agoApplications go to the employer, never to RoleSprint
About this role
At TreviPay, we believe loyalty begins at the payment. Thousands of sellers use our global B2B payments and invoicing network to provide choice and convenience to buyers, open new markets and automate accounts receivables. With integrations to top eCommerce and ERP solutions and flexible trade credit options, TreviPay brings 40 years of experience serving leaders in manufacturing, retail and transportation.
Every day, TreviPay employees are challenged and empowered in a supportive, collaborative, entrepreneurial environment.
At TreviPay, we believe loyalty begins at the payment. Thousands of sellers use our global B2B payments and invoicing network to provide choice and convenience to buyers, open new markets and automate accounts receivables. With integrations to top eCommerce and ERP solutions and flexible trade credit options, TreviPay brings 40 years of experience serving leaders in manufacturing, retail and transportation.
Every day, TreviPay employees are challenged and empowered in a supportive, collaborative, entrepreneurial environment.
As a Senior Decision Science Analyst, you'll sit at the intersection of data, risk, and business strategy. You'll use analytics and predictive modeling to uncover insights, evaluate portfolio performance, and guide critical credit, fraud, and pricing decisions. Partnering with teams across the organization, you'll help solve complex business challenges, influence strategic initiatives, and deliver data-driven solutions that support growth while managing risk. Responsibilities
• Deliver and communicate high quality data-driven analyses to key stakeholders and senior management that provide key insights leading to actionable results. • Access, cleanse, and analyze relevant internal and external data to support the creation, monitoring, and improvement of effective B2B credit risk management and pricing strategy techniques across for existing account management. • Conduct data exploration, data validation, and data audits to identify and address data quality issues and recommend improvements. • Support Engineering and Product teams with resolution of roadblocks and interdependencies. • Analyze risk and pricing strategies, including the predictive models built for those strategies in Credit and Fraud. • Monitor the results of risk and pricing strategies by evaluating performance relative to expectations. • Develop monitoring tools to evaluate continued performance of both generic and custom models • Support the development (or build yourself) of statistical models and other types of predictive models as appropriate to improve our Credit and Fraud risk position, for both application and portfolio risk.
Requirements:
• Bachelor’s Degree Required • Minimum 6 years of proven work experience in a highly analytical environment performing complex business analyses, generating data-driven insights and presenting findings to leadership and other stakeholders. • Strong knowledge of B2B credit and/or Business Banking credit risk, pricing, and profitability principles • Ability to deal with ambiguity and be flexible enough to shift workload in accordance with changing priorities. • Ability to extract, cleanse, merge and analyze data from varied internal and external sources. • Strong analytical and data simulation skills including SAS and/or Python, MS Excel, Enterprise Reporting BI tools, or similar analytical and reporting/data visualization packages. • Strong presentation skills and proficiency in MS Word and PowerPoint • Experience in analyzing segments of data or utilizing tools to identify and explain patterns, trends and/or process improvements • Ability to create clear, concise graphs, charts, reports and presentations summarizing analytical results and justifying suggested improvements • High performing contributor with ability to collaborate cross-functionally with management, product, technology, compliance and enterprise risk • The ability to multitask in a fast-paced environment • Strong communication skills, both verbal and written Preferred Qualifications:
• Bachelor’s or Master’s Degree in Statistics, Mathematics or similar quantitative field of study • Strong knowledge of B2B and/or Business Banking credit product pricing and profitability principles • Statistical modeling experience (logistic regression, machine learning, SVM, and more) • Prior leadership experience
Why you will love working at TreviPay Competitive salary Paid parental leave Generous paid time off Medical, dental, vision, FSA, Life/AD&D, long and short term disability 401K matching Employee referral program At TreviPay we believe: in saying yes to unique and challenging requirements empowered team members are creative team members our products make the customer’s day just a little bit better work/life balance makes us all more effective TreviPay is an Equal Opportunity and Affirmative Action Employer. We welcome all veterans and disabled applicants.
Individuals with disabilities will be provided reasonable accommodation to participate in the job application and/or interview process. Please contact Recruiting@trevipay.com to request an accommodation.
Work location
- Overland Park, KS, US
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About this listing
Published on Lever under the board identifier Trevipay, which is the name the employer’s own job board carries. RoleSprint has not verified the company’s registered or trading name, so it is shown exactly as published rather than tidied up.
RoleSprint is not the employer and not a recruiter. Applications are made on the employer’s own site and never reach us; what RoleSprint does is help you decide whether a role is worth your time and prepare for it if it is.
Published 28 August 2026, last checked 28 days ago. A posting stops being advertised here 90 days after the employer published it, and one the employer takes down is marked closed rather than quietly removed.