One Park Financial

Provides working capital for small businesses

Senior Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours

Full-timePlano, TX, USANo salary listed

About the Job

Requirements

The candidate must have 4–7 years of experience in data science and the financial industry, preferably in credit or lending.
The candidate must have a master's degree in mathematics, statistics, computer science, or data science.
The candidate must have experience transforming existing processes with artificial intelligence or machine-learning approaches.
The candidate must be proficient in data manipulation and have strong SQL and Python skills for data wrangling and extract, transform, and load (ETL).
The candidate must have hands-on AWS or cloud experience deploying and operating production machine-learning services, including compute, storage, identity and access management, and containerized deployment.
The candidate must have experience building or maintaining production applications and services, including owning software in production rather than working only with notebook models.
The candidate must have experience with dashboard tools such as Power BI or other visualization tools.
The candidate must have experience building credit or risk models for financial services, lending, or insurance.
The candidate must have experience validating models to identify ongoing improvements.
The candidate must have data science skills in exploratory data analysis, feature engineering, fitting, tuning, and comparing models, and managing the model lifecycle.
The candidate must have experience with statistical modeling and data analysis using programming languages such as Python.
The candidate must have experience in machine-learning engineering, cloud-based deployment, and machine-learning model lifecycle management.

Responsibilities

Use advanced statistical and machine-learning techniques to analyze large datasets and build artificial intelligence and machine-learning models.
Develop and pressure-test pricing and credit proposals end to end, applying statistical and analytical rigor to validate them before launch; proposals may include well-tested policy changes rather than models.
Conduct exploratory data analysis, feature engineering, and data preprocessing to solve business problems.
Own, operate, and enhance the proprietary risk-based pricing engine, a production Python application, including its models, business logic, deployment, and monitoring.
Facilitate deployment and monitoring of models for real-time and batch processing.
Maintain model governance through 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 rollout from staging to production, including continuous integration and continuous deployment, monitoring, and rollback.
Regularly evaluate and validate models to ensure robust performance.
Design and run scientifically rigorous A/B tests, gather test data, validate results, and present findings to business stakeholders.
Use modern artificial intelligence and large language model tools to accelerate exploratory data analysis, feature engineering, model prototyping, documentation, and testing.
Identify creative uses of existing data to solve business problems.
Produce statistical and data-analysis visuals, including charts and infographics, to communicate findings to non-technical audiences.
Collaborate with team members, product managers, and business stakeholders to identify opportunities for new artificial intelligence and machine-learning solutions.
Analyze areas including onboarding credit, ongoing credit, marketing segmentation, voice-based analysis, text mining, sentiment analysis, risk quantification, and risk-based pricing.

Desired Qualifications

Experience with credit or lending is preferred within the financial industry experience requirement.
Knowledge of best practices for financial and lending models, including model risk management, model governance, and fair-lending considerations, is a plus.
Comfort using modern artificial intelligence and large language model tools to make analysis and modeling more efficient is a plus.
Experience with dbt is a major plus.

Senior Data Scientist

One Park Financial · Plano, United States (Hybrid) · Full-time · Posted 2026-08-20

Workplace: hybrid

Department: Analytics

Description

Company Overview:

One Park Financial (OPF) is a leading Financial Technology company dedicated to empowering small businesses by connecting them with a wide variety of flexible financing and funding options. Our mission is to provide entrepreneurs with the working capital they need to elevate their businesses to new heights. At OPF, we believe in working with high-performing individuals who are ready to play an integral part in our company's expansion. We know that our success hinges on our people, and we strive to enable them to do what they do best.

Why Join Us?

At OPF, we foster a dynamic and inclusive company culture that emphasizes collaboration, innovation, and personal growth. Our team is composed of passionate, driven individuals who are committed to making a difference. Here's what you can expect when you join our team:

Innovative Environment: Work with cutting-edge technology and be part of a team that is constantly pushing the boundaries of fintech.
Professional Growth: We invest in our employees' growth with continuous learning opportunities, training programs, and career advancement paths.
Supportive Culture: Enjoy a supportive and inclusive work environment where your ideas are valued, and your contributions make a real impact.
Community Focus: Be part of a company that understands the importance of small and mid-sized businesses to their communities and the nation's financial health.
High-Performing Team: Join a team of badasses who are committed to excellence and are integral to our company's expansion and success.

About the Role

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.

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.

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.
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).

Benefits

Competitive salary
Local & National Health Insurance
Dental and Vision insurance
Group Medical Bridge
401k with Match
ID Protection: 100% covered by the company
Life Insurance: 100% covered by the company
Generous PTO and holidays
Growth and development opportunities
Dynamic and collaborative work environment
Company events and team-building activities

Apply

Apply at One Park Financial

About the Company

One Park Financial

Provides working capital for small businesses

One Park Financial provides working capital for small and mid-sized businesses. By drawing on direct experience with entrepreneurs, they help owners access funding options that fit their needs, enabling growth and operations. Their products are designed to supply working capital through funding options tailored to SMBs, rather than a one-size-fits-all loan. The company differentiates itself through a deep understanding of small business challenges, a focus on community impact, and recognition as a Top Workplace for several years, underscoring a people-first approach. The goal is to help small business owners succeed on their own terms by giving them affordable, accessible capital to support their initiatives and contribute to local economies.

Company size: 51-200
Company stage: Not listed
Total funding: Not listed
Headquarters: Miami, FL
Founded: 2010

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