Role: Data Scientist
Onsite
Pittsburgh, PA
Key Responsibilities:
- Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
- Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.
- Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
- Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.
- Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.
- Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.
- Work with large-scale structured and unstructured datasets to derive actionable insights and improve operational performance.
- Partner with engineering and MLOps teams to deploy and monitor production-grade ML/RL models.
- Mentor junior data scientists and promote best practices in modeling, experimentation, and AI governance.
Must-Have Qualifications:
- Strong experience in Reinforcement Learning and sequential decision-making systems, using algorithms such as Q-Learning, DQN, PPO, Bandits, etc.
- Experimentation and simulation frameworks.
- Strong programming skills in Python and SQL.
- Experience with Databricks, Spark, or similar big data/cloud analytics platforms.
- Experience building scalable ML pipelines and deploying models into production environments.
- Ability to communicate complex AI/ML concepts to technical and non-technical stakeholders.
Preferred Skills:
- Experience in collections, credit risk, customer analytics, or financial services domains.
- Familiarity with deep Learning frameworks such as TensorFlow and PyTorch
- MLOps and CI/CD workflows
- Cloud platforms such as AWS, Azure, or Google Cloud Platform
- Exposure to causal inference, uplift modeling, or optimization techniques.
- Knowledge of customer lifecycle analytics and behavioral segmentation.
- Experience working in Agile delivery environments.
1+ years of work experience with Reinforcement Learning