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About the Role
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.
Responsibilities
- 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.
Requirements
- Experience in Data Science, Machine Learning, AdTech (Preferred), Marketing Science, or a related field is required.
- 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 decisions.
Skills
- Machine Learning
- AdTech
- Marketing Science
- Ad Bidding Systems
- RTB Optimization
- Budget Pacing
- Bid Shading
- Auction-based Decisioning
- Personalization Systems
- Recommendation Systems
- Targeting Systems
- Incrementality Measurement
- Experimentation
- A/B Testing
- Causal Inference
- Attribution Modeling
- Digital Advertising Ecosystem
- DSPs
- SSPs
- Ad Exchanges
- Identity Solutions
- Targeting Strategies
- Measurement Methodologies
- Python
- Data Manipulation
- Feature Engineering
- Model Development
- Apache Spark
- Production Deployment
- Batch Pipelines
- Real-time Pipelines
- APIs
- Monitoring
- Model Lifecycle Management
- Statistics
- Machine Learning Algorithms
- Optimization Techniques
- Predictive Modeling
- AWS
- Azure
- GCP
- ML Infrastructure
- MLOps Tools
- Communication
- Stakeholder Management
Location
- San Francisco, California
Work Type
- Onsite
Experience Level
- Senior
Salary/Compensations
- $138,000 - $221,000 USD
Benefits
- 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
- 5 personal days
- 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
About the Company
- 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.
- Everyone wants easier ways to pay; we invent them.
- Checkout lines are slow; we speed them along.
- Merchants want more sales; we give them data and insights.
- People need financial access; we connect them.
- Corporate purchasing is complicated; we make it simple.
- Commuters are busy; we speed them on their way.
- Governments need greater efficiencies; we help create them.
- Small businesses are virtual; we give them access to a world of buyers.
- Retailers want to fight fraud; we provide the tools.
Equal Opportunity
- 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.