Data Scientist Analyst

World Business Lenders, LLC.; Northern (KY); Hybrid; USD 90,000 - 140,000; Full time

Benefits offered by this job

Fully remote

Job summary

World Business Lenders, LLC. is hiring a data scientist to join the Data team in developing predictive models for loan performance, defaults, and collateral valuation.

You will work with large real estate and lending datasets, applying machine learning and statistical techniques to forecast outcomes and support risk decisions. The role emphasizes ownership, autonomy, and clear communication of complex analyses to stakeholders, including senior management.

Qualifications

• Bachelor's or Master's degree in a data-related field.
• 3–7 years of data science, modelling, or quantitative analytics.
• Remote-friendly, English-language CV required.

Responsibilities

• Analyze and validate credit, risk, valuation, and recovery models against real historical data.
• Develop predictive/forecasting models for loan performance, defaults, and collateral value.
• Design backtests, sensitivity analyses, scenario comparisons, and time-based analyses.
• Work with large, multi-source datasets to extract, clean, reconcile, and validate data.
• Identify factors predictive of performance and build user-friendly tools for stakeholders to explore results.
• Investigate data quality issues, edge cases, model limitations, and inconsistencies.
• Translate findings into actionable underwriting, pricing, and risk management recommendations.
• Maintain reproducible, well-documented analytical work for review and extension.

Skills

Python

SQL

Data Science

Education

Bachelor's or Master's in Data Science/Statistics/Math/Finance/Economics

Job description

About World Business Lenders

• At World Business Lenders (WBL), we provide flexible, short-term commercial loans backed by real estate to help small and medium-sized businesses across the United States — particularly those facing difficulties with traditional financing. We're a fast-moving, results-driven organization that takes security seriously as we continue to grow.
• This is a Full-Time Independent Contractor role with working hours from 9:00 AM - 6:00 PM Eastern Standard Time, Monday through Friday. We request that all CVs be submitted in English.

About the Role

• This role sits within WBL's Data team and focuses on credit, loan performance, collateral, and recovery modeling, helping the business better understand and predict how loans and their underlying real estate collateral behave over their life cycle.
• The work involves large, national real estate and lending datasets. This is real data at scale work, often involving millions of records and large, imperfect, real world files.
• A growing part of this role is building predictive and forecasting models using historical, realized outcomes to estimate future loan performance, default and payoff behavior, collateral value changes, recovery timing, costs, and expected outcomes. This includes applying machine learning and statistical techniques where appropriate, not just descriptive or backward-looking analysis.
• This role will contribute to the development and validation of WBL's internal loan valuation and risk models and comes with a high degree of ownership and autonomy as our data and modeling capabilities expand. .

Typical Day-to-Day

• Early on, the day starts by joining the daily BLV alignment meeting to stay current on the project's philosophy, direction, and where things currently stand.
• Most of the day is spent hands-on with data: pulling and cleaning records from large national real estate and lending datasets, cross-checking fields across multiple sources, and investigating data quality issues as they come up. A meaningful part of this involves directing AI-assisted tools to run deep, multi-step analyses efficiently, while carefully validating their output rather than accepting it at face value.
• Several days a week involve model-focused work: running back tests of internal valuation and risk models against real, historical outcomes, building or refining predictive/forecasting components, and running sensitivity analyses.
• Regularly, the role also involves presenting and communicating analyses and findings clearly to the team and stakeholders, translating technical results into actionable takeaways

What You'll Be Doing (Key Responsibilities)

• Analyze and validate internal credit, risk, valuation, and recovery models against real, historical loan and portfolio outcomes
• Build predictive and forecasting models that estimate future loan performance, default or payoff timing, collateral value changes, recovery outcomes, costs, and timelines
• Design and run backtests, sensitivity analyses, scenario comparisons, and time-based analyses across large historical datasets
• Work with large, multi-source lending and real estate datasets to extract, clean, reconcile and validate data for analysis and modelling
• Identify the factors most predictive of loan performance, collateral outcomes, and realised recoveries, and build simple tools that let stakeholders explore model results and scenarios
• Investigate and clearly document data quality issues, edge cases, model limitations, and inconsistencies found in large, real-world datasets
• Translate analytical and modelling findings into clear, actionable recommendations for underwriting, credit, pricing, portfolio management, and risk management
• Maintain clean, reproducible, well documented analytical and modelling work so it can be independently reviewed, validated, or extended by others.

Education:

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Finance, Economics, or a related field.

Years of Experience:

• 3 to 7 years of experience in data science, applied modelling, or quantitative analytics, ideally involving large, real-world datasets.

Remote Work Requirements

• Stable, reliable internet connection.
• Professional and dedicated remote working setup.

Preferred Background / Industry Experience:

• Background or strong interest in finance, banking, commercial lending, U.S. real estate, or property valuation.
• Familiarity with commercial or small business lending is a plus.
• Experience with predictive modelling, forecasting, survival/time-to-event analysis, or other time-based estimation problems.
• Experience with credit risk, default, loss, recovery, model validation, or scenario/sensitivity analysis is a plus
• Experience reconciling and cleaning data from multiple sources or systems.

Key Soft Skills:

• Fast learner, intellectually curious, comfortable getting up to speed quickly in unfamiliar domains
• Detail-oriented without losing sight of the bigger picture
• Strong data communication skills, able to explain complex analytical and modelling findings clearly to both technical and non-technical audiences, including senior management
• Comfortable working with real autonomy and ambiguity, and proactive about asking the right questions
• Rigorous, skeptical mindset, doesn't take a number at face value without checking it independently.

Specific Technical Skills Needed:

• Strongproficiencyin Python and SQL
• Experience with statistical modelling, machine learning, forecasting, or related quantitative techniques, including model evaluation and validation
• Experience working with large datasets, including writing efficient, performance-conscious data processing code
• Experience building simple, shareable analytical tools or dashboards is a plus

What We Offer

Compensation in USD.

Benefitsinclude paid time off (PTO).

Work Environment:Fully remote work environment.