Manager, AI & Data Science - Cognitive AI

Your opportunity

At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together. We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s). 

Are you all about leveraging data to provide customers better products and grow business? We are looking for qualified candidates to design and implement scalable data-driven solutions for dynamic financial products, deliver business insights from unstructured data with clear narratives and creative visualizations, and partner closely with the data infrastructure and platforms teams to develop tools and automation. We have a large and diverse set of data, sourced from our extensive client base and huge volume of transactions across multiple channels. This is a great opportunity to apply your craft as a data scientist while working with business partners to drive data-driven solutions.

The Schwab Cognitive AI team leads the development, deployment, and maintenance of AI/ML solutions powered by advanced NLP and NLU technologies. Our work supports hundreds of products and serves millions of financial clients across Schwab’s business ecosystem. As the core data science and model development team, we specialize in applying state-of-the-art techniques in natural language processing and understanding to deliver scalable, intelligent solutions. 

As a Data Scientist IV, you will work collaboratively with a team of product managers, data scientists, and engineers, throughout the project lifecycle including data extraction and preparation, design and implementation of algorithms, and creating solutions that solve real world problems in the following areas. 

NLP & NLU: 
Deep understanding of NLP and NLU, with hands-on experience in building both classical and state-of-the-art transformer-based models for intent extraction, sentiment analysis, and query expansion. Proven expertise in data and feature engineering on large, messy user transcript datasets—including cleaning, lemmatization, and building high-quality corpora—to support model development and continual improvement. Experience in training and iteratively refining intent classification systems using labeled data and user feedback to drive data-informed decision-making.  

Statistical Methodology: 
Strong foundation in statistical theory and application, including hypothesis testing, Bayesian inference, experimental design, A/B testing, power analysis, and statistical modeling. Skilled in selecting and applying appropriate statistical techniques to evaluate model performance, understand data distributions, and ensure robustness and reproducibility of results in real-world scenarios. 

Machine Learning: 
Expertise in classical ML algorithms (e.g., GLM, Random Forest, Gradient Boosting, SVM) as well as advanced transformer-based architectures for NLP applications (e.g., BERT, RoBERTa, ModernBERT). Demonstrated ability to design and deploy deep learning solutions tailored to text mining and user behavior modeling. Should be able to fine-tune and evaluate encoder-only and encoder-decoder models using techniques like LoRA, PEFT, quantization, knowledge distillation, and RLHF. Experienced in applying Graph Neural Networks (GNNs) to build scalable recommendation systems and uncover relationships in structured and unstructured datasets. Architect ML workflows using MLflow and integrate with CI/CD pipelines on cloud platforms. Optimize and scale training on GPU clusters using distributed training frameworks 

Business Acumen: 
Strong ability to bridge the gap between technical outputs and business needs. Skilled at distilling complex analytics into actionable insights for diverse stakeholders. Proactively engages with product, marketing, and operations teams to align AI/ML strategies with organizational goals—ensuring that solutions deliver measurable business impact. Understands how to go beyond stakeholder asks to uncover latent needs and opportunities. 

What you have

Required Qualifications

• Master’s degree in Statistics, Mathematics, Computer Science, Operations Research, Engineering or similar quantitative field. 
• 7+ years of experience in NLP/NLU, building both classical and transformer-based models (e.g., BERT, RoBERTa) for intent detection, sentiment analysis, and query expansion. 
• 7+ years of experience with advanced proficiency in Python, Pytorch, Tensorflow, Hugging Face transformers and scikit-learn 
• 4+ years of deep understanding of transformer architectures, pretraining vs. fine-tuning, attention mechanisms and encoder models 
• 7+ years of experience in statistics, including A/B testing, hypothesis testing, and Bayesian methods for robust model evaluation. 
• 7+ years of experience applying classical ML (e.g., XGBoost, Random Forests) and deep learning, including Graph Neural Networks for recommender systems. 
• 4+ years of experience working with messy, unstructured, and multi-source text data and building high-throughput data processing pipelines 
• 2+ years of experience in model deployment using GCP, Kubernetes and model serving frameworks 
• 4+ years of experience using MLOps best practices, including versioning, monitoring, model drift detection, and retraining triggers  

Preferred Qualifications

• Graduate degree in Statistics, Mathematics, Computer Science, Operations Research, Engineering or similar quantitative field.
• Experience with Agentic AI, LLM orchestration frameworks (Langchain, Llamaindex etc.)
• Exposure to Google Dialogflow or other conversational AI platforms
• Experience in Dataiku, Streamlit, Flask or Dash for quick prototyping
• Experience working in a regulated industry
• Sharp business acumen, able to align technical solutions with strategic goals and drive impact across cross-functional teams.
• Demonstrated leadership mindset, with a bias for action, deep dive mentality, critical thinking, and a commitment to raising the bar—while
• knowing when to respectfully disagree and commit for the greater good of the team and mission 

In addition to the salary range, this role is also eligible for bonus or incentive opportunities.