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).

Requirements

• 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

• 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 MLOps: CI/CD pipelines, model monitoring, cloud platforms, Deployment strategy
• Expertise in Emerging Techniques: LLM fine-tuning, federated learning, automated feature engineering, siamese networks, backbones (feature extraction networks), efficient transformer architectures.
• Experience in Personalization: Session-based and long term interest recommendations. Two-Tower and Transformer based architectures
• Experience in Lead Scoring / Behavior: Predictive analytics, churn modeling, and causal ML for attribution.

Benefits

• health insurance (medical, dental, vision, prescription)
• 401(k) matching
• paid time off
• parental leave
• sign-on payments and restricted stock units (RSUs)