Trepp is looking to solve complex business problems in commercial real estate and structured finance by designing, developing, and deploying advanced data science and GenAI solutions.
Requirements
- Advanced proficiency in Python for data analysis, machine learning, and GenAI development.
- Strong SQL skills and experience working with large, complex datasets.
- Experience with modern ML libraries and frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
- Proven experience with LLMs and GenAI tooling, including: prompt engineering and evaluation, embeddings and semantic search, retrieval-augmented generation (RAG) architectures, model APIs and open-source LLM frameworks
- Experience deploying models and GenAI services using cloud platforms (e.g., AWS).
- Proficiency with GitHub and collaborative development workflows.
- Experience with large language models (LLMs), retrieval-augmented generation (RAG), embeddings, agentic architectures and prompt engineering techniques.
Responsibilities
- Partner with product managers, engineers, and domain experts to translate ambiguous business problems into scalable, data-driven solutions.
- Architect and implement GenAI applications leveraging large language models (LLMs), retrieval-augmented generation (RAG), embeddings, agentic architectures and prompt engineering techniques.
- Build, evaluate, and maintain machine learning models using appropriate statistical, ML, and deep learning approaches.
- Ensure model quality through rigorous experimentation, validation, monitoring, and performance evaluation.
- Communicate complex technical concepts, results, and trade-offs clearly to both technical and non-technical stakeholders.
- Provide technical mentorship and guidance to junior data scientists, contributing to best practices and team standards.
- Contribute to the evolution of Trepp’s data science and GenAI strategy, tools, and methodologies.
Other
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field.
- 5+ years of professional data science or applied machine learning experience.
- 2+ years of hands-on experience developing, deploying, and maintaining GenAI solutions in production environments.
- Strong analytical thinking and problem-solving ability, with attention to detail.
- Ability to independently own complex projects and manage ambiguity.
