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About This Role
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
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At One Park Financial, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
One Park Financial AI Hiring
One Park Financial has 1 open AI role right now. They're hiring across Data Scientist. Based in Miami, FL, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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