Data Scientist

Bold Penguin•Dublin, OH

3d•Remote

About The Position

As a Data Scientist at Bold Penguin, you will be at the forefront of building our next generation of intelligent insurance products. The Data Scientist will be responsible for designing and deploying sophisticated AI systems that bridge the gap between Generative AI and Quantitative Finance. You will move beyond standard model deployment to build multi-agent frameworks and orchestration layers capable of complex financial reasoning. This role requires a strong background in Optimization Theory and Portfolio Management, similar to the algorithmic modeling found in high-growth FinTech or property analytics environments. You will oversee the end-to-end data pipeline, from cleaning unstructured financial data to retraining models to mitigate bias and ensure high-fidelity market insights.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related quantitative field such as Financial Engineering.
3 to 5 years of experience as a Data Scientist, preferably within Financial Services, FinTech, or Property Risk Analytics.
Proven experience with Large Language Models (LLMs), agentic orchestration, and RAG architectures.
Solid understanding of optimization algorithms, statistical analysis, and machine learning principles.
Strong proficiency in Python and familiarity with AWS SageMaker and Bedrock.
Proficiency with enterprise data science platforms such as RapidMiner (Altair) for automating financial data pipelines and risk modeling.
Exceptional analytical skills with a focus on attention to detail and data integrity.
Strong communication skills and the ability to work effectively across technical and product teams.

Responsibilities

Apply mathematical optimization and portfolio theory to solve complex resource allocation and market placement problems.
Engineer Retrieval-Augmented Generation (RAG) pipelines and manage vector databases to provide real-time, context-aware intelligence.
Design and implement multi-agent systems and orchestration layers to automate complex, multi-step financial workflows.
Evaluate model performance using advanced statistical techniques to ensure accuracy in high-stakes financial scenarios.
Utilize AWS SageMaker and Bedrock to build, train, and scale machine learning models and agentic tools.