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Cecl Loss Forecasting Jobs (NOW HIRING)

Truist

Head of Objective Forecasting

Atlanta, GA · On-site

The Objective Forecasting team supports the models used in stress testing (CCAR), the allowance for credit loss (ACL/CECL), market valuation and earnings at risk (IRR), and market risk. This support ...

Truist

Head of Objective Forecasting

Richmond, VA · On-site

The Objective Forecasting team supports the models used in stress testing (CCAR), the allowance for credit loss (ACL/CECL), market valuation and earnings at risk (IRR), and market risk. This support ...

Truist

Head of Objective Forecasting

Charlotte, NC · On-site

The Objective Forecasting team supports the models used in stress testing (CCAR), the allowance for credit loss (ACL/CECL), market valuation and earnings at risk (IRR), and market risk. This support ...

Alliant Credit Union

Lead Model Validation

Chicago, IL · Hybrid

$95K - $163K/yr

... loss forecasting, stress testing, ALM, AML, fraud, pricing, and CECL models. * Apply sound analytical judgment to evaluate model input data, conceptual soundness, performance, implementation, and ...

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Cecl Loss Forecasting information

What is CECL loss forecasting?

CECL loss forecasting refers to the process of estimating credit losses under the Current Expected Credit Loss (CECL) accounting standard. This involves projecting future credit losses for financial assets such as loans, based on historical data, current conditions, and reasonable forecasts. CECL requires institutions to recognize expected lifetime losses at the time of asset origination or purchase, rather than waiting for losses to become probable. Accurate CECL loss forecasting helps banks and lenders maintain appropriate reserves and comply with regulatory requirements.

What skills and qualifications are needed for a CECL loss forecasting analyst?

To thrive as a CECL Loss Forecasting Analyst, you need strong quantitative analysis skills, knowledge of accounting standards (especially CECL), and a background in finance or statistics, often supported by a relevant degree. Proficiency with statistical modeling tools (such as SAS, R, or Python), data visualization platforms, and experience with financial reporting systems are typically required. Attention to detail, critical thinking, and clear communication are crucial soft skills for interpreting complex data and presenting findings to stakeholders. These skills and qualities ensure accurate loss forecasts, regulatory compliance, and informed decision-making within financial institutions.

What are common challenges in CECL loss forecasting roles and how can they be addressed?

Professionals in CECL (Current Expected Credit Loss) Loss Forecasting often encounter challenges such as managing large volumes of complex data, keeping up with evolving regulatory requirements, and ensuring model accuracy under varied economic scenarios. To address these challenges, it's important to stay current with industry best practices, collaborate closely with cross-functional teams like risk management and IT, and engage in regular model validation and documentation. Leveraging advanced analytics tools and participating in ongoing training can also help professionals stay effective and compliant in this dynamic field.

What is the difference between Cecl Loss Forecasting vs Credit Risk Analyst?

AspectCecl Loss ForecastingCredit Risk Analyst
Required CredentialsBachelor's degree in finance, economics, or related field; familiarity with accounting standardsBachelor's degree in finance, economics, or related field; analytical skills
Work EnvironmentFinancial institutions, banks, or credit organizations focusing on loss estimationBanks, lending institutions, or credit agencies assessing borrower risk
Industry UsagePrimarily in banking and financial services for loan loss provisioningAcross banking, lending, and credit sectors for risk assessment

Cecl Loss Forecasting specializes in estimating expected credit losses using accounting standards like CECL, focusing on loss provisioning. Credit Risk Analysts evaluate borrower creditworthiness and assess risk, often using similar data but with a broader scope. While both roles require financial analysis skills, Cecl Loss Forecasting is more focused on loss estimation models, whereas Credit Risk Analysts handle overall credit risk assessment.

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Infographic showing various Cecl Loss Forecasting job openings in the United States as of August 2026, with employment types broken down into 72% Full Time, 26% Part Time, 1% Temporary, and 1% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution.

Director Quant Engineer, Finance Analytics

Webster Bank

Stamford, CT • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Webster Bank rating

7.1

Company rating: 7.1 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

124th of 174 rated banks

Job description

If you're looking for a meaningful career, you'll find it here at Webster Bank, a division of Santander Bank, N.A.. Founded in 1935, our focus has always been to put people first--doing whatever we can to help individuals, families, businesses and our colleagues achieve their financial goals. As a leading commercial bank, we remain passionate about serving our clients and supporting our communities. Integrity, Collaboration, Accountability, Agility, Respect, Excellence are Webster's values, these set us apart as a bank and as an employer.
Come join our team where you can expand your career potential, benefit from our robust development opportunities, and enjoy meaningful work!
The role involves gathering and understanding FP&A business requirements; Webster's data systems and tools; designing coding and maintaining data workflows; anticipating and resolving data challenges; user-friendly reports and dashboards; and clearly communicating results. The individual needs to collaborate with IT, Data Team, and LoB partners in developing data workflows, data analysis and reporting. The scope includes working across one or more of our main verticals: R&D, Quarterly Production, and/or Credit Scorecards. The Quant Engineer role is right for you if you're a "hands-on" coder and analyst, who takes ownership and puts the business first.
Primary Responsibilities
  • Gather, analyze, and document FP&A business requirements and translate them into scalable analytical and technical solutions.
  • Design, develop, implement, and maintain robust data workflows supporting financial planning, forecasting, reporting, and credit analytics.
  • Lead the development, implementation, and maintenance of CECL credit loss models supporting regulatory reserving, stress testing, and underwriting activities.
  • Personally participate in coding, testing, deployment, and maintenance of analytical models, workflows, and production processes.
  • Develop deep expertise in Webster's data platforms, systems, tools, and reporting environments.
  • Partner with IT, Data Management, Finance, Risk, and business stakeholders to develop data architectures, workflows, and reporting solutions.
  • Create intuitive reports, dashboards, and data visualizations that enable effective decision-making across the organization.
  • Anticipate, identify, and resolve data quality, workflow, and operational challenges impacting model and reporting processes.
  • Maintain comprehensive workpapers and development documentation supporting governance, audit, validation, and regulatory requirements.
  • Author technical documentation detailing model design, development, testing, implementation, and ongoing enhancements.
  • Effectively communicate analytical findings, technical concepts, and business insights to both technical and non-technical audiences.
  • Lead and/or participate in discussions with Executive Management, Model Risk Management, Audit, Regulatory, and cross-functional project teams.
  • Drive continuous improvement initiatives by identifying opportunities to automate processes and enhance analytical capabilities.

Required Skills & Experience
  • 10+ years of software engineering, data engineering, analytics, or quantitative modeling experience within a commercial bank or financial institution.
  • Strong experience working with complex relational databases and large-scale data structures using Oracle, SQL, or similar technologies.
  • Advanced proficiency in Python, SAS, and/or R programming languages.
  • Experience designing, implementing, and maintaining efficient and scalable data workflows.
  • Experience with workflow orchestration tools such as Apache Airflow or similar platforms.
  • Strong understanding of software development lifecycle methodologies, coding standards, testing frameworks, and deployment practices.
  • Experience building analytical solutions supporting financial, credit risk, forecasting, or reporting functions.
  • Knowledge of commercial and consumer banking products, processes, and operations.
  • Strong analytical, problem-solving, and critical-thinking capabilities.
  • Demonstrated ability to manage multiple priorities and deliver high-quality results in a fast-paced environment.
  • Excellent written and verbal communication skills with the ability to explain complex concepts to diverse audiences.
  • Strong attention to detail and commitment to data quality and accuracy.

Preferred Skills & Experience
  • Experience supporting CECL, stress testing, underwriting, loss forecasting, or credit risk analytics functions.
  • Knowledge of dashboarding and reporting tools such as Tableau, Qlik Sense, or similar business intelligence platforms.
  • Experience supporting model governance, validation, audit, and regulatory review processes.
  • Strong understanding of financial planning and analysis processes within a banking environment.
  • Experience developing enterprise-scale data platforms and analytical solutions.
  • Proven ability to collaborate effectively across Finance, Risk, IT, Data Management, and business organizations.
  • Strong project leadership and stakeholder management skills.
  • Experience driving process automation and workflow optimization initiatives.
  • Familiarity with model risk management frameworks and regulatory expectations.

Education
  • Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, Mathematics, Statistics, Finance, Economics, or a related quantitative field required.
  • Master's degree in a quantitative discipline preferred.
  • Equivalent combinations of education, technical training, and relevant professional experience will be considered.

The estimated salary range for this position is $160,000 USD to $180,000 USD. Actual salary may vary up or down depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position is eligible for incentive compensation.
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Santander Holdings USA, Inc. and its subsidiaries ("Santander") are equal opportunity employers committed to sustaining an inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, marital status, national origin, ancestry, citizenship, sex, sexual orientation, gender identity and/or expression, physical or mental disability, protected veteran status, or any other characteristic protected by law.

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