Senior Applied Scientist, Measurement, Ad Tech, and Data Science (MADS)

About the Team and Role

The Measurement, Ad Tech, and Data Science (MADS) group at Amazon Ads is dedicated to creating next-generation solutions that assist millions of advertisers in understanding their advertising ROI while safeguarding privacy and measurement integrity. The Media Planning Science subgroup specializes in building and deploying models that generate insights and strategic recommendations for media planning and measurement across Amazon's advertising portfolio.

The team tackles a variety of challenges, including Reach and Frequency analysis, Budget Optimization, and Recommendation Systems, utilizing heuristic and machine learning methods such as deep learning. Outputs are delivered through agent-based tools and APIs, enabling seamless integration into user interfaces and programmatic environments to optimize advertising performance.

Key Responsibilities

• Lead creation of comprehensive media planning models covering advertisers' full investment scope, emphasizing scalability and efficiency.
• Work collaboratively with cross-functional partners—engineering, product, and business teams—to define and deploy measurement solutions.
• Leverage cutting-edge scientific techniques including Generative AI, traditional machine learning, causal inference, NLP, and computer vision to build models assessing media plan impacts.
• Drive continuous model enhancement through iterative experimentation, testing, and optimizations.
• Translate complex scientific problems into clear, actionable insights for business stakeholders.
• Mentor and support junior scientists fostering a culture of collaboration and high performance.
• Promote inter-scientist collaboration to accelerate progress with broad impact.
• Engage regularly with the scientific community via presentations, publications, and patent contributions.

Typical Workday

Your daily activities will involve processing and analyzing large datasets to solve practical problems, generating insights and business opportunities, designing experiments and simulations, and creating ML/DL models. Collaboration with scientists, engineers, and product managers is essential, alongside communicating findings clearly to audiences with varying technical backgrounds.

Team Composition

The team comprises experts in Applied Science, Research, and Data Science disciplines who bring specialized knowledge in machine learning, deep learning, NLP, Generative AI, and causal inference. They collaborate closely with engineers, product leaders, sales teams, and advertising domain scientists to develop scalable modeling and software solutions.

Qualifications

• Minimum 3 years’ experience developing machine learning models for business use cases.
• PhD or Master’s degree with at least 6 years of applied research experience.
• Proficient in programming languages such as Python, Java, or C++.
• Experience with neural deep learning frameworks and machine learning methodologies.

Preferred Skills

• Familiarity with modeling and data science tools including R, scikit-learn, Spark MLLib, MxNet, TensorFlow, numpy, scipy.
• Expertise working with large-scale distributed computing systems like Hadoop and Spark.
• Advanced degree (PhD) in relevant quantitative fields such as engineering, technology, computer science, machine learning, operations research, statistics, or mathematics.

Additional Information

Amazon is committed to equal opportunity employment and does not discriminate based on veteran status, disability, or other protected statuses. The company promotes an inclusive culture that enables employees to deliver excellent results. Accommodations are available during application and hiring processes for those who require them.

The compensation range for this role in Toronto, Canada is CAD 195,900 to 327,200 annually. The total package may include sign-on bonuses and restricted stock units (RSUs). Comprehensive benefits include medical, dental, vision, prescription coverage, life and AD&D insurance, registered retirement savings plans, deferred profit sharing, paid leave, and additional wellness resources.