What are the responsibilities and job description for the Senior Applied Scientist - Predictive Scoring, AWS Marketing Science position at Amazon Web Services, Inc.?
DESCRIPTION
As a Senior Applied Scientist specializing in lead scoring and deep learning modeling, you will tackle complex challenges in machine learning and deep learning to redefine how our business engages with customers. You will design and deploy high-impact models that drive customer segmentation, adaptive recommendations, and predictive lead and account prioritization. Leveraging your expertise in deep learning, representation learning, and general modeling, you'll help build solutions that directly influence business outcomes, collaborating with cross-functional teams to turn novel research into scalable, production-grade systems.
Key job responsibilities
About the team
The AWS Marketing Science team builds the ML models and measurement systems that drive marketing decisions across Amazon Web Services. We own incrementality and valuation, ROI measurement, marketing attribution, propensity scoring, account and lead clustering, and next-best-action models. Our work directly influences how AWS allocates marketing spend, targets accounts, and measures effectiveness across billions in pipeline.
Key job responsibilities
- Design and deploy predictive lead scoring models to optimize customer acquisition, conversion, and retention strategies using advanced techniques like survival analysis, graph networks, or transformer-based architectures.
- Architect end-to-end ML pipelines for large-scale deep learning models, including data preprocessing, distributed training, model optimization, and real-time inference.
- Publish research, file patents, and stay ahead of industry trends in the marketing science, propensity modeling, and customer journey prediction domains.
- Innovate in multi-modal modeling (text, graph, behavioral, and temporal data) to enhance scoring accuracy across account and lead levels.
- Conduct rigorous A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate rapidly.
- Collaborate with MLOps engineers to streamline model deployment, monitoring, and retraining using tools like AWS SageMaker, or MLflow and other internal tools.
- Participate in science reviews to raise the science bar in our organization. This includes reviewing your work and the work of others.
- Mentor junior scientists on ML methodology, experimentation design, and production best practices.
- Define offline and online evaluation frameworks; establish success metrics tied to business outcomes (conversion rates, pipeline generation).
About the team
The AWS Marketing Science team builds the ML models and measurement systems that drive marketing decisions across Amazon Web Services. We own incrementality and valuation, ROI measurement, marketing attribution, propensity scoring, account and lead clustering, and next-best-action models. Our work directly influences how AWS allocates marketing spend, targets accounts, and measures effectiveness across billions in pipeline.
BASIC QUALIFICATIONS
- 3 years of building machine learning models for business application experience
- PhD, or Master's degree and 6 years of applied research experience
- Experience programming in Java, C , Python or related language
- Experience with neural deep learning methods and machine learning
- Knowledge of deep learning, machine learning and statistics
- Experience engaging, verbally and in writing, with internal and external stakeholders to convey complex ideas in a clear, concise manner
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or equivalent)
- Real world experience in recommender systems, transformers, or multi-objective tasks.
- Strong background in statistical analysis, experimental design, and SQL/Spark for big data processing
- Extensive knowledge in a breadth of machine learning topics
PREFERRED QUALIFICATIONS
- Proven success in deploying deep learning models (e.g., BERT/Transformers for NLP/behavioral sequences, diffusion models, GANs or general DNNs) to solve business problems.
- Publications or patents in applied ML domains
- Expertise in at least one focus area in each of the following:
- **MLOps**: CI/CD pipelines, model monitoring, cloud platforms, Deployment strategy
- **Emerging Techniques**: LLM fine-tuning, federated learning, automated feature engineering, siamese networks, backbones (feature extraction networks), efficient transformer architectures.
- Experience in at least one focus area in either of the following:
- **Personalization**: Session-based and long term interest recommendations. Two-Tower and Transformer based architectures
- **Lead Scoring / Behavior**: Predictive analytics, churn modeling, and causal ML for attribution.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, TX, Austin - 167,100.00 - 226,100.00 USD annually