The Manager, Data Scientist role focuses on advanced recommenders and personalization systems built to deliver real-time, tailored experiences across digital channels. In this McLean, VA onsite position, you will lead work spanning transformer- and LLM-based modeling, reinforcement learning, and scalable production deployment for recommendation and arbitration systems.
What you’ll do
- Design and develop state-of-the-art recommender systems using transformers, large language models (LLMs), and reinforcement learning.
- Advance personalization through sequence modeling, multi-modal learning, and contextual decision-making, including contextual Multi-Armed Bandits.
- Build and fine-tune foundation models trained on large-scale customer interaction data.
- Use reinforcement learning to optimize for long-term user engagement and business outcomes.
- Create state-of-the-art evaluation frameworks to measure the efficacy of campaigns and recommendation systems.
- Collaborate with engineering and product teams to deploy scalable, production-grade, low-latency systems.
- Drive innovation in emerging areas such as generative recommendations, conversational systems, and cross-domain personalization (including travel and shopping).
Team and impact
You will be part of a high-impact initiative building next-generation large-scale reinforcement learning-based recommender systems and arbitration engines. The systems are intended to power personalized experiences across marketing, customer servicing, and digital products, leveraging rich credit and customer behavioral data from tens of millions of customers across Mobile, Web, and Email.
The role supports the development of a foundational system expected to reach a large portion of the U.S. population and influence how millions of people discover, decide, and engage across financial and lifestyle experiences.
Technologies and tools
- Modeling and ML: transformers, large language models (LLMs), reinforcement learning, sequence modeling, multi-modal learning, contextual Multi-Armed Bandits, foundation models, distributed training
- Programming and frameworks: Python, PyTorch, TensorFlow, open-source tools, Scala, R
- Data and systems: cloud computing platforms, AWS, SQL, relational databases
- Analytics concepts: confusion matrix, ROC curve, clustering, classification, sentiment analysis, time series, deep learning
Salary and location
- McLean, VA (onsite): USD 197,300 - 225,100 per year
Benefits
- Eligible to earn performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
- Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being.
Education
Bachelor’s degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or another related quantitative field).
Minimum qualifications
- Currently has, or is in the process of obtaining one of the following with the expectation that the required degree will be obtained on or before the scheduled start date:
- A Bachelor’s Degree in a quantitative field plus 6 years of experience performing data analytics
- A Master’s Degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of experience performing data analytics
- A PhD in a quantitative field plus 1 year of experience performing data analytics
- At least 1 year leveraging open source programming languages for large scale data analysis
- At least 1 year working with machine learning
- At least 1 year utilizing relational databases
- At least 1 year of experience working with AWS
- At least 4 years of experience in Python, Scala, or R for large scale data analysis
- At least 4 years of experience with machine learning
- At least 4 years of experience with SQL
Preferred qualifications
- PhD in a “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
- At least 1 year of experience working with AWS
- At least 4 years experience in Python, Scala, or R for large scale data analysis
- At least 4 years experience with machine learning
- At least 4 years experience with SQL
