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Statistician - Director, Asset Backed Finance

Job in Charlotte, Mecklenburg County, North Carolina, 28245, USA
Listing for: Barings LLC
Full Time position
Listed on 2026-02-16
Job specializations:
  • Finance & Banking
    Data Scientist
Salary/Wage Range or Industry Benchmark: 150000 - 200000 USD Yearly USD 150000.00 200000.00 YEAR
Job Description & How to Apply Below
At Barings, we are as invested in our associates as we are in our clients. We recognize those who work diligently for us and reward them for personal and professional integrity, communication skills, distinct competencies and expertise in specific strategies, ability to collaborate as a team member and true dedication to the interests of our clients.

We thank you for your interest in joining the Barings team, and invite you to explore our current employment opportunities.
*
* Title:

** Statistician/Data Scientist - Director
** Corporate

Title:

** Director
* * Department**:
Residential - ABF
*
* Location:

** Charlotte, NC
*** Job Summary
***** Barings ABF group
** specializes in  a range of public and private asset based investments, primarily residential mortgage related. Our team combines deep industry expertise with advanced analytics to make informed investment decisions and create value for our clients. We are seeking a talented and experienced Statistician to join our analytics and modeling team to support the company's asset-based investment strategies, with an initial focus on residential mortgage assets.

The Statistician will be responsible for conducting advanced statistical analysis, sourcing new and relevant data for internal databases, developing and implementing predictive models, and supporting decision-making in the asset-based investment space. This role requires expertise in statistical modeling, data analysis, and machine learning, with the ability to work closely with cross-functional teams to extract insights from complex consumer and mortgage-related data.

The position will reside in our Charlotte, NC office.
*** Primary Responsibilities
**** Develop and implement statistical models to analyze trends, risks, and opportunities in residential mortgage investments.
* Design, manage and optimize large-scale datasets, (e.g., historical mortgage data, borrower behavior, housing market data, interest rate changes, and other macroeconomic factors) to ensure high-quality data availability for modeling, surveillance and analysis.
* Oversee the integration of data from various sources, ensuring data quality, accuracy, and consistency.
* Apply machine learning algorithms such as regression trees, random forests, support vector machines, and neural networks to enhance predictive capabilities.
* Collaborate with internal teams (e.g., portfolio managers, capital market analysts, risk managers, and structurers) to interpret model results and provide actionable insights for investment decision-making.
* Conduct risk analysis and stress testing using both traditional and machine learning-based methods to evaluate portfolio resilience under various economic scenarios.
* Communicate complex statistical and machine learning concepts to non-technical stakeholders.
* Conduct deep learning and AI-based model experimentation to improve accuracy and scalability of mortgage performance forecasting as well as improve servicing oversight.
* Stay up-to-date with the latest developments in statistical methodologies, machine learning, artificial intelligence, and trends within the mortgage and investment sectors.
*** Qualifications
* *** Master's or Ph.D. degree in Statistics, Mathematics, Engineering, Data Science, or a related field.
* 5+ years of experience in statistical modeling, data analysis, and/or quantitative finance within a financial field, with direct experience in residential mortgages or similar assets a plus.
* Expert proficiency in statistical analysis software and coding (e.g., R, SAS, Python, MATLAB).
* Strong knowledge of statistical modeling techniques such as regression analysis, time-series modeling, survival analysis, and machine learning.
* Proficiency in machine learning algorithms (e.g., random forests, gradient boosting machines, support vector machines, neural networks) and frameworks
* Experience with data manipulation and visualization tools (e.g., SQL, Tableau, R, Power BI).
* Proven ability to analyze large, complex datasets and extract meaningful insights that drive strategic decisions.
* Strong problem-solving skills with the ability to design solutions for modeling complex issues, such as…
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