Senior Data Scientist

One Park Financial•Miami, FL

23h

About The Position

We are looking for a seasoned Senior Data Scientist to join our Analytics team to enable core business transformation. This role is about building and pressure-testing the models and proposals that drive how we price, approve, and grow, and doing it with the statistical and analytical rigor to prove they work before they ship. You will own a production system end to end, helping operate and evolve our proprietary risk-based pricing engine, the application that powers our real-time offer decisioning. You will work closely with business stakeholders to understand problems and propose & implement AI/ML solutions, and you will partner with DevOps, Product, and Engineering to take models from notebook to production. You will lead the charge in A/B testing within the organization and design experiments to prove success. You will perform EDA, identify modeling opportunities, feature engineer & ETL data, implement models and their monitoring, and highlight opportunities for change. We are an AI-forward company, and we expect our data scientists to work that way. We want someone who leans on modern AI and LLM tooling to make their own analysis, modeling, and experimentation faster and sharper, not someone who does things the slow, manual way. We want to work with high-performing badasses who will play an integral part in our transformation of the company. We understand one thing: it all comes down to working with creative & committed people and enabling them to do what they do best.

Requirements

4-7 years of experience in Data Science and the Financial Industry, preferably in Credit or Lending.
Master's degree in mathematics, statistics, computer science, or data science.
Experience in transforming existing processes with AI/ML-based approaches.
Proficiency in data manipulation. Excellent SQL and Python skills for data wrangling and ETL.
Hands-on AWS / cloud experience deploying and operating production ML services (compute, storage, IAM, containerized deployment).
Experience building or maintaining production applications and services, not just models in notebooks. You should be comfortable owning software in production.
Experience with dashboard tools such as PowerBI or other visualization tools.
Experience building credit or risk models for Financial Services, Lending, or Insurance.
Experience in validating models to identify ongoing improvements.
Rock Solid data science skillset: Exploratory Data Analysis, Feature Engineering, Fitting, Tuning, and Comparing models, and managing the model Lifecycle.
Experience with statistical modeling and data analysis using programming languages such as Python.
Experience in ML engineering, cloud-based deployment, and machine learning model lifecycle management.

Nice To Haves

Knowledge of best practices for financial and lending models (model risk management, model governance, and fair-lending considerations) is a big plus.
Comfort using modern AI and LLM tools to make your own analysis and modeling more efficient (a plus).
Experience with dbt (major plus).

Responsibilities

Utilize advanced statistical and machine learning techniques to analyze large datasets and build new AI/ML models.
Develop and pressure-test pricing and credit proposals end to end, with the statistical and analytical rigor to prove they will work before they ship. These won't always be models; sometimes the answer is a well-tested policy change.
Conduct exploratory data analysis, feature engineering, and data preprocessing to solve business problems.
Own, operate, and enhance our proprietary risk-based pricing engine (a production Python application), including its models, business logic, deployment, and monitoring.
Facilitate the deployment and monitoring of models for real-time and batch processing.
Own strong model governance: clear documentation and versioning, ongoing monitoring for drift and degradation, regular validation, and a defensible audit trail across the model lifecycle.
Partner with DevOps, Product, and Engineering teams to ship models and features to production, owning the rollout from staging to production, including CI/CD, monitoring, and rollback.
Perform model evaluation and validation on a regular basis to ensure robust performance.
Engineer A/B tests with scientific rigor. Gather test data and validate results to present to business stakeholders.
Use modern AI and LLM tooling to speed up your own work, from EDA and feature engineering to model prototyping, documentation, and testing.
Champion creative uses of existing data to solve business problems with intellectual curiosity.
Produce statistical and data analysis visuals (charts, infographics) to communicate findings clearly and effectively to a non-technical audience.
Collaborate with team members, product managers, and business stakeholders to identify opportunities for new and innovative AI/ML solutions.
Analysis areas could include Onboarding Credit, Ongoing Credit, Marketing segmentation, Voice-based analysis, Text mining, Sentiment analysis, Risk quantification, and Risk-based pricing.