The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
Quick apply
The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
... or data science with a focus on credit risk, fraud, or financial services * Hands-on experience ... with LLMs, embeddings models, or transformer-based architectures -- not just familiarity, but ...
... or data science with a focus on credit risk, fraud, or financial services * Hands-on experience ... with LLMs, embeddings models, or transformer-based architectures -- not just familiarity, but ...
... or data science with a focus on credit risk, fraud, or financial services * Hands-on experience ... with LLMs, embeddings models, or transformer-based architectures - not just familiarity, but ...
... or data science with a focus on credit risk, fraud, or financial services * Hands-on experience ... with LLMs, embeddings models, or transformer-based architectures - not just familiarity, but ...
... or data science with a focus on credit risk, fraud, or financial services * Hands-on experience ... with LLMs, embeddings models, or transformer-based architectures -- not just familiarity, but ...
... or data science with a focus on credit risk, fraud, or financial services * Hands-on experience ... with LLMs, embeddings models, or transformer-based architectures -- not just familiarity, but ...
The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
Quick apply
The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
Director of Data Science
San Francisco, CA · On-site
$225K - $250K/yr
Experience applying predictive modeling to credit risk assessment, churn prediction, loss and ... data science productivity, quality, and business impact. The anticipated annual base salary for ...
Director of Data Science
San Francisco, CA · On-site
$225K - $250K/yr
Experience applying predictive modeling to credit risk assessment, churn prediction, loss and ... data science productivity, quality, and business impact. The anticipated annual base salary for ...
Analytics Manager, Credit Cards
Los Angeles, CA · On-site +1
What you'll bring: * 6+ years of experience in Analytics, Data Science, Decision Science, Credit Risk, or a related quantitative function. * Must have 4+ years supporting Credit Card portfolios, with ...
Analytics Manager, Credit Cards
Los Angeles, CA · On-site +1
What you'll bring: * 6+ years of experience in Analytics, Data Science, Decision Science, Credit Risk, or a related quantitative function. * Must have 4+ years supporting Credit Card portfolios, with ...
Credit Risk Strategy Manager / Senior Manager
San Francisco, CA · On-site
$150K - $210K/yr
Partner with Data Science to define feature requirements, evaluate model performance, and translate ... Familiarity working with credit risk models (including regression and tree-based machine learning ...
Credit Risk Strategy Manager / Senior Manager
San Francisco, CA · On-site
$150K - $210K/yr
Partner with Data Science to define feature requirements, evaluate model performance, and translate ... Familiarity working with credit risk models (including regression and tree-based machine learning ...
... data - to build proprietary risk attributes and models. * Own credit risk AI science for the consumer lending and fast-money portfolio - including first-generation and next-generation credit risk ...
... data - to build proprietary risk attributes and models. * Own credit risk AI science for the consumer lending and fast-money portfolio - including first-generation and next-generation credit risk ...
... data - to build proprietary risk attributes and models. * Own credit risk AI science for the consumer lending and fast-money portfolio - including first-generation and next-generation credit risk ...
... data - to build proprietary risk attributes and models. * Own credit risk AI science for the consumer lending and fast-money portfolio - including first-generation and next-generation credit risk ...
Credit Risk Practice Lead
San Francisco, CA · On-site
$202K - $280K/yr
The individual needs to have strong knowledge of the lending business, data and domain across the ... Master's or similar in Economics, Statistics, Computer Science/Engineering, Operations Research, or ...
Credit Risk Practice Lead
San Francisco, CA · On-site
$202K - $280K/yr
The individual needs to have strong knowledge of the lending business, data and domain across the ... Master's or similar in Economics, Statistics, Computer Science/Engineering, Operations Research, or ...
Credit Risk Practice Lead
San Francisco, CA · On-site
$202K - $280K/yr
The individual needs to have strong knowledge of the lending business, data and domain across the ... Master's or similar in Economics, Statistics, Computer Science/Engineering, Operations Research, or ...
Credit Risk Practice Lead
San Francisco, CA · On-site
$202K - $280K/yr
The individual needs to have strong knowledge of the lending business, data and domain across the ... Master's or similar in Economics, Statistics, Computer Science/Engineering, Operations Research, or ...
Credit Risk Practice Lead
$202K - $280K/yr
The individual needs to have strong knowledge of the lending business, data and domain across the ... Master's or similar in Economics, Statistics, Computer Science/Engineering, Operations Research, or ...
Credit Risk Practice Lead
$202K - $280K/yr
The individual needs to have strong knowledge of the lending business, data and domain across the ... Master's or similar in Economics, Statistics, Computer Science/Engineering, Operations Research, or ...
... credit risk models for next-generation financial products. This role combines deep technical ... Required Qualifications * Advanced degree (MS/PhD) in Computer Science, Data Science, AI ...
... credit risk models for next-generation financial products. This role combines deep technical ... Required Qualifications * Advanced degree (MS/PhD) in Computer Science, Data Science, AI ...
Manager, Credit Risk Operations
San Francisco, CA · On-site
$130K - $160K/yr
You'll leverage data-driven insights to enhance our credit decisioning and monitoring frameworks ... Evaluate credit risk across payment acceptance channels (cards, bank payments, local payment ...
Manager, Credit Risk Operations
San Francisco, CA · On-site
$130K - $160K/yr
You'll leverage data-driven insights to enhance our credit decisioning and monitoring frameworks ... Evaluate credit risk across payment acceptance channels (cards, bank payments, local payment ...
Advanced proficiency in data analysis tools (e.g. SQL) * Familiarity with core risk domains such as card brand monitoring, compliance, and KYB/KYC requirements * Experience managing credit exposure ...
Advanced proficiency in data analysis tools (e.g. SQL) * Familiarity with core risk domains such as card brand monitoring, compliance, and KYB/KYC requirements * Experience managing credit exposure ...
Credit Strategy Manager
San Francisco, CA · On-site
... and risk and operational data science and analytics. The team designs data-driven strategies to ... The Credit Strategy Lead will work in the Credit team and have responsibilities to analyze and ...
Credit Strategy Manager
San Francisco, CA · On-site
... and risk and operational data science and analytics. The team designs data-driven strategies to ... The Credit Strategy Lead will work in the Credit team and have responsibilities to analyze and ...
The Role The Data Science team at Block turns unique customer and product data into decisions that ... credit systems often miss. You'll partner closely with product, engineering, and risk teams to ...
The Role The Data Science team at Block turns unique customer and product data into decisions that ... credit systems often miss. You'll partner closely with product, engineering, and risk teams to ...
Credit Risk Data Science information
What is credit risk data science?
Credit Risk Data Science is a specialized field that uses statistical analysis, machine learning, and data modeling techniques to assess and predict the likelihood that a borrower will default on a loan or credit obligation. Professionals in this field analyze large datasets from financial transactions, credit reports, and market trends to develop models that help financial institutions make informed lending decisions. Their work helps manage risk, set appropriate interest rates, and comply with regulatory standards. By leveraging advanced analytics, credit risk data scientists play a crucial role in minimizing losses and maximizing profitability for banks and lenders.
What skills and qualifications are needed to thrive as a credit risk data scientist?
To thrive as a Credit Risk Data Scientist, you need strong analytical skills, proficiency in statistical modeling, and a solid background in finance, mathematics, or a related field, often supported by an advanced degree. Familiarity with programming languages like Python or R, experience with machine learning frameworks, and knowledge of credit risk modeling tools such as SAS or SQL are typically required. Critical thinking, attention to detail, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These abilities are crucial for building accurate risk models, informing strategic decisions, and ensuring regulatory compliance in financial institutions.
How does a credit risk data scientist typically collaborate with other teams within a financial institution?
Credit Risk Data Scientists often work closely with credit analysts, risk managers, and IT professionals to develop, validate, and implement models that assess borrower risk. They frequently participate in cross-functional meetings to translate complex analytical findings into actionable business insights. Collaboration with compliance and regulatory teams is also common to ensure that risk models meet current regulatory standards. Effective communication and teamwork are essential, as the role bridges technical model development and practical risk management decisions.
What are popular job titles related to Credit Risk Data Science jobs in California?
For Credit Risk Data Science jobs in California, the most frequently searched job titles are:
What job categories do people searching Credit Risk Data Science jobs in California look for?
The top searched job categories for Credit Risk Data Science jobs in California are:
What cities in California are hiring for Credit Risk Data Science jobs?
Cities in California with the most Credit Risk Data Science job openings:
Credit Risk Strategy Manager
San Francisco, CA • Hybrid
Inizio Partners
Full-time
Posted 15 days ago
Job description
Credit Risk Strategy Manager
Location: San Francisco Bay Area
Work Mode: Hybrid (2-3 days/week in office)
Role Summary:
The role will support the analysis, development and optimization of credit policies and strategies, with a focus on underwriting, credit line / loan amount assignment, and risk-based pricing. The individual will use analytical tools and data-driven insights to identify opportunities for profitable portfolio growth while effectively managing credit risk across the customer lifecycle.
Responsibilities:
- Assist in the analysis, development and monitoring of credit policies, including underwriting, line assignment and pricing strategies.
- Analyze credit and portfolio data to identify opportunities for risk-adjusted growth and recommend policy changes.
- Develop and optimize underwriting criteria, credit line assignment and risk-based pricing strategies.
- Evaluate the impact of credit policy changes through portfolio analysis, scenario testing and experiments.
- Translate credit policies and strategies into business rules using SQL and Python.
- Monitor key credit risk and business performance metrics and proactively identify emerging trends.
- Collaborate with Risk, Product, Technology and Operations teams to implement credit policy changes.
Requirements:
- 6+ Years of experience in Credit Risk
- Strong analytical skills with hands-on experience in SQL; working knowledge of Python preferred.
- Experience in credit risk strategy, credit policy, underwriting or portfolio analytics within Banking / Financial Services.
- Good understanding of credit underwriting, line assignment / credit limit strategies and risk-based pricing.
- Ability to analyze customer, application, bureau and portfolio data to identify trends and develop credit policy recommendations.
- Experience in evaluating the impact of policy changes on approval rates, credit losses, profitability and portfolio growth.
- Understanding of credit risk concepts such as eligibility criteria, risk segmentation, cut-offs, affordability, exposure management and pricing.
- Ability to define and monitor relevant risk and business KPIs to measure strategy performance.
- Experience with strategy testing, scenario analysis and A/B testing is preferred.
- Knowledge of external credit data sources such as credit bureaus, FICO or similar third-party data is preferred.
- Strong problem-solving skills with the ability to translate analytical findings into actionable business recommendations.
- Ability to communicate and present analyses and recommendations effectively to business and risk stakeholders.
- Knowledge of coding best practices, data manipulation and development of reusable analytical frameworks.