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Principal Data Scientist (, MA, United States)

$165.3k - $249.68k
Full-time

Global Payments Inc

Job Description

This position is not eligible for visa sponsorship, now or in the future. Candidates must be a US Citizen or Green Card Holder. This position is remote in the Greater Boston Area only or hybrid 3 days per week in our Cincinnati, OH or Atlanta, GA office. Ready to take your career global? Make your mark at one of the biggest names in payments. We are seeking an experienced and visionary Principal Data Scientist to help scale our Data Science and AI team. In this role, you will collaborate closely with ML software engineers, product managers, and other product delivery teams to build ML models and data-driven algorithms into robust, scalable, and production-ready products that optimize the end-to-end payment process. You will act as payment subject matter expert and apply strong system and product thinking to create new product concepts, influence and build development roadmaps, design intelligent decision engines, drive cross functional initiatives and mentor senior DS/ML engineers. You will balance strategic leadership with hands-on technical contribution, dividing your time between setting technical direction and actively participating in architecture, design reviews, code reviews, and selected implementation efforts.

What You’ll Own

  • Define the technical vision and strategy for new data driven product initiatives with a focus on end-to-end payment optimization, aligning them with business goals.
  • Develop scalable and reusable capabilities to power real-time machine learning models (classification, ranking and optimization), enable rapid experimentation at scale, and support continuous monitoring of ML products.
  • Oversee and participate hands-on in the conceptualization, development, production deployment, operation, validation, and maintenance of ML models and model update systems.
  • Establish and ensure adherence to best practices for ML modeling. Stay abreast of industry trends and emerging technologies in ML and payment optimization, driving adoption of modern tools, frameworks, and infrastructure.
  • Foster a culture of innovation, experimentation and collaboration. Mentor an elite team of data scientists.
  • Partner with product, engineering, data, and compliance teams to integrate ML into products. Communicate complex technical concepts clearly to both technical and non-technical audiences.
  • Collaborate with other Data Science leaders to establish an operating model for machine learning R&D that optimizes end-to-end delivery of business value.

What You’ll Bring

  • Bachelor's or master's degree in computer science, Statistics, Mathematics, Engineering, or related field (PhD preferred).
  • 7+ years in ML research or engineering; 5+ years building large-scale, real-time ML systems in production, including significant experience hands-on.
  • Expertise in machine learning, statistical modeling, and data analysis, strong experience across the ML lifecycle and experimentation processes.
  • Proficiency in Python (Pandas, NumPy, scikit-learn), strong SQL and database experience (NoSQL and/or graph database experience is a plus).
  • Demonstrated ability to lead solution design and execution, translating complex business requirements into actionable technology solutions.
  • Exceptional verbal and written communication skills, with the ability to influence and collaborate effectively across technical and business audiences at all levels, including executive leadership.

It’s a bonus if you have

  • Experience in payments, fintech, or financial services.
  • Knowledge of payment systems (authorization lifecycle, tokenization, fraud/risk, real-time payments).
  • Experience with cloud platforms (e.g., AWS, GCP, Snowflake, Databricks, Vertex AI).
  • Agile development experience.
  • Experience in large companies, mature ML teams, and/or highly regulated industries.

About The Team

Our inclusive and global teams win together every day. We’re proud to have the best minds in the industry, who you can learn from as you grow your career. The people, the energy, the connections – it’s unmatched. Come and be part of an ever-evolving company and get dynamic opportunities that go beyond borders.

What makes a Globalpayer

Globalpayers think like a client, act like an owner and win as one team. We’re curious and innovative – always finding better ways to deliver impact. We empower each other to make decisions, and it’s our passion that drives excellence in everything we set out to do.

Worldpay is dedicated to offering individuals rewarding career opportunities and competitive compensation. For this full-time position, the good faith estimated annual salary range upon hire is $165,300.00-$249,675.00. This range reflects what we reasonably expect to offer based on the role’s responsibilities, level, and geographic location. The actual starting salary will be determined by a candidate’s experience, job-related skills, and relevant education or training. Please note that changes in work location may impact the final offered salary. We encourage you to consult with your recruiter to confirm the budget for your location and to better understand the applicable pay scale.

EEOC Statement

Worldpay is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here.

If you are made a conditional offer of employment and will be working in the United States, you will be required to undergo a drug test. In developing this job description care was taken to include all competencies and requirements needed to successfully perform the position. Reasonable accommodations will be provided for individuals with qualified disabilities both during the hiring process, as well as to allow the individual to perform the essential functions of the job, if hired.

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Vacancy posted 12 hours ago
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