Senior Data Scientist

Posted Yesterday
San Francisco, CA, USA
Hybrid
138K-221K Annually
Senior level
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
We are a global technology company in the payments industry.
The Role
Design, build, and deploy ML models for ad targeting, ranking, and bidding optimization. Develop large-scale personalization and recommendation systems, attribution and incrementality testing frameworks, and experiment strategies. Partner with product, engineering, and analytics to productionize low-latency models, optimize performance and scalability, and apply causal inference and advanced measurement across the AdTech ecosystem.
Summary Generated by Built In
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Senior Data Scientist
Overview:
We are seeking a Senior Data Scientist to design, develop, and deploy machine learning models that power Ad targeting, ranking, and bidding optimization in real-time or near real-time environments. This role will be instrumental in advancing Mastercard's commerce media and AdTech capabilities through data-driven personalization, measurement, and optimization solutions.
About the Role:• Design, build, and deploy machine learning models for ad targeting, ranking, and bidding optimization.• Develop and scale personalization and recommendation systems using large-scale transaction and behavioral datasets.• Lead the design and implementation of incrementality testing frameworks, including lift measurement and causal inference methodologies, to evaluate campaign effectiveness.• Build and enhance attribution models, including multi-touch and probabilistic attribution approaches across channels and devices.• Partner closely with product, engineering, analytics, and business teams to translate business objectives into scalable data science solutions.• Optimize machine learning models for accuracy, performance, latency, and scalability in production environments.• Contribute to the architecture, design, and evolution of Mastercard's AdTech and commerce media products.• Drive experimentation strategies, including A/B testing and advanced measurement frameworks.• Stay current with emerging trends and technologies across the AdTech ecosystem, including DSPs, SSPs, RTB protocols, identity resolution, privacy-preserving techniques, and digital advertising measurement.
All About You• Experience in Data Science, Machine Learning, AdTech (Preferred), Marketing Science, or a related field is required for this position.• Proven experience building and optimizing ad bidding systems, including RTB optimization, budget pacing, bid shading, and auction-based decisioning.• Hands-on expertise developing personalization, recommendation, and targeting systems at scale.• Strong background in incrementality measurement, experimentation, A/B testing, causal inference, and advanced attribution modeling.• Deep understanding of the digital advertising ecosystem, including DSPs, SSPs, ad exchanges, identity solutions, targeting strategies, and measurement methodologies.• Proficiency in Python and related data science libraries, with strong experience in data manipulation, feature engineering, and model development.• Experience working with large-scale distributed data processing frameworks such as Apache Spark.• Demonstrated success deploying machine learning models into production environments, including batch and real-time pipelines, APIs, monitoring, and model lifecycle management.• Strong foundation in statistics, machine learning algorithms, optimization techniques, and predictive modeling.• Experience leveraging cloud platforms such as AWS, Azure, or GCP, along with modern ML infrastructure and MLOps tools.• Excellent communication and stakeholder management skills, with the ability to explain complex analytical concepts to both technical and non-technical audiences.• Ability to thrive in a fast-paced, highly collaborative environment and influence technical and business
This position is currently only available to local candidates in the San Francisco area. Relocation is not currently available for this position.
Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact [email protected] and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
  • Abide by Mastercard's security policies and practices;
  • Ensure the confidentiality and integrity of the information being accessed;
  • Report any suspected information security violation or breach, and
  • Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.

In line with Mastercard's total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best-in-class company match; deferred compensation for eligible roles; fitness reimbursement or on-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on-site fitness facilities in some locations.
Pay Ranges
San Francisco, California: $138,000 - $221,000 USD

Skills Required

  • Experience in Data Science, Machine Learning, Marketing Science, or related field
  • Proven experience building and optimizing ad bidding systems (RTB optimization, budget pacing, bid shading, auction decisioning)
  • Hands-on expertise developing personalization, recommendation, and targeting systems at scale
  • Strong background in incrementality measurement, A/B testing, causal inference, and advanced attribution modeling
  • Deep understanding of digital advertising ecosystem (DSPs, SSPs, ad exchanges, identity solutions, targeting, measurement)
  • Proficiency in Python and data science libraries for data manipulation, feature engineering, and model development
  • Experience with large-scale distributed data processing frameworks such as Apache Spark
  • Demonstrated success deploying ML models to production (batch and real-time pipelines, APIs, monitoring, model lifecycle management)
  • Strong foundation in statistics, machine learning algorithms, optimization techniques, and predictive modeling
  • Experience with cloud platforms (AWS, Azure, or GCP) and modern ML infrastructure / MLOps tools
  • Excellent communication and stakeholder management skills
  • Ability to work in fast-paced, highly collaborative environments and influence technical and business partners
  • Must be a local candidate in the San Francisco area (relocation not available)

What the Team is Saying

Jenny
Mastercard

Mastercard Compensation & Benefits Highlights

  • Retirement Support A 10% company retirement match (401k or equivalent) is explicitly highlighted in company materials. This level of employer contribution stands out as a core strength of the package.
  • Leave & Time Off Breadth A global minimum of 16 weeks fully paid new‑parent leave and generous U.S. PTO (vacation, personal days, holidays, sick time, and bereavement) are clearly spelled out. These provisions indicate broad time‑off coverage across life events.
  • Wellbeing & Lifestyle Benefits Hybrid work, a four‑week “work from elsewhere” option, meeting‑free well‑being days, five paid volunteer days, mental‑health resources, and fitness reimbursement/on‑site gyms are emphasized. Together they reflect a holistic approach to flexibility and wellbeing.

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The Company
HQ: Purchase, NY
38,800 Employees
Year Founded: 1966

What We Do

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Why Work With Us

We live the Mastercard Way: creating value in the communities we touch, growing together through the opportunities we see, and moving fast to innovate and scale. Our collaborative culture and our passionate people are the key to what we do, driving meaningful change as one team and connecting everyone to priceless possibilities.

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Employees engage in a combination of remote and on-site work.

In our ongoing workplace evolution, we’ve introduced hybrid work, Work-From-Elsewhere Weeks and Meeting-Free Days.

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