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Credit Risk Data Science Jobs in Fremont, CA (NOW HIRING)

Brigit

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 ...

SoFi

Credit Strategy Manager

San Francisco, CA

... 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 ...

Apple

WPC Apple Card Data Scientist

Cupertino, CA · On-site

You should have experience and subject matter expertise in financial modeling, payments, banking, credit, risk operations, data science, and finance. Previous experience working in the credit card ...

SoFi

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 ...

Visa

Director, VCA NA Risk Practice

Foster City, CA · On-site

This role requires deep expertise in credit risk frameworks, and data analytics, with a strong ... Partner closely with data science teams to deploy and interpret predictive models, model forecasts ...

Visa

Director, VCA NA Risk Practice

San Francisco, CA · On-site

This role requires deep expertise in credit risk frameworks, and data analytics, with a strong ... Partner closely with data science teams to deploy and interpret predictive models, model forecasts ...

Visa

Director, VCA NA Risk Practice

San Francisco, CA · On-site

This role requires deep expertise in credit risk frameworks, and data analytics, with a strong ... Partner closely with data science teams to deploy and interpret predictive models, model forecasts ...

Apple

WPC Apple Card Data Scientist

Cupertino, CA

$175K - $311K/yr

You should have experience and subject matter expertise in financial modeling, payments, banking, credit, risk operations, data science, and finance. Previous experience working in the credit card ...

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Showing results 21-40

All JobsCredit Risk Data Science JobsCredit Risk Data Science Jobs in Fremont, CA

Credit Risk Data Science information

See Fremont, CA salary details

$40.5K

$124.7K

$216.2K

How much do credit risk data science jobs pay per year?

As of Aug 6, 2026, the average yearly pay for credit risk data science in Fremont, CA is $124,662.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,300.00 and $153,800.00 per year, depending on experience, location, and employer.

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 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.
What are popular job titles related to Credit Risk Data Science jobs in Fremont, CA? For Credit Risk Data Science jobs in Fremont, CA, the most frequently searched job titles are:
What job categories do people searching Credit Risk Data Science jobs in Fremont, CA look for? The top searched job categories for Credit Risk Data Science jobs in Fremont, CA are:
What cities near Fremont, CA are hiring for Credit Risk Data Science jobs? Cities near Fremont, CA with the most Credit Risk Data Science job openings:
Credit Risk Data Science jobs near you
Infographic showing various Credit Risk Data Science job openings in Fremont, CA as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $124,662 per year, or $59.9 per hour.

Senior ML Data Scientist (Credit Risk)

Zed Financial PH, Inc

San Francisco, CA • On-site

Full-time

Re-posted 21 days ago

Job description

About Zed
Zed is building the first AI-native, licensed neobank in the Philippines designed to democratize access to premium financial services for young professionals in global markets. The current banking system is broken, often shutting out the world's youngest and fastest-growing consumer classes-we're here to fix it.
Our team is uniquely positioned to solve this. We are Stanford engineers and former YC founders who have spent our careers at the intersection of banking and hyper-growth startups like Square, Facebook, and Box. We've been here before, having previously built and exited Symple (YC W'17), a fast-growing B2B payments company.
We are backed by world-class investors, including Accel, Valar, Immad Akhund (Mercury), Dalton Caldwell (Y Combinator), and Kunal Shah (Cred).
The Role
Zed underwrites credit using foundation models that profile risk from transaction data, financial documents, and other structured and unstructured sources - not credit scores. That means our ML stack looks less like a traditional bank's and more like a modern AI system: embedding models, transformer architectures, and LLM-assisted data pipelines sitting alongside classical credit and fraud models. We're hiring a Senior ML Data Scientist to work directly with our data lead across all of it - core credit models, account management, and fraud detection - with a particular focus on pushing the frontier of how we represent and reason about financial data. This is a senior role, which means we expect you to have opinions about the stack, shape how we build, and set the technical bar for ML at Zed as the team grows. If you're the kind of person who's deploying neural networks and transformer-based models in production rather than just reading about them, this role was written for you.
What You'll Do
  • Work closely with our data lead on the full risk model suite: core credit decisioning, account management, and fraud detection
  • Own data preparation pipelines for model inputs - including using NNs and LLMs to represent transaction data as vector embeddings for quantitative analysis
  • Experiment with and deploy neural network and transformer-based architectures in the underwriting process
  • Build agent scaffolding and harnesses within underwriting workflows - this is active, in-production experimentation, not research
  • Design and deploy fraud detection models combining rule-based systems and ML to identify suspicious activity in real time
  • Engineer features from structured and unstructured data sources
  • Develop monitoring systems to keep models accurate and reliable in production
  • Partner with engineering and risk operations to integrate model outputs into decisioning systems
  • Influence technical direction - you'll have a seat at the table when we make decisions about how ML is built and deployed at Zed
What You Bring
  • 7+ years of experience in applied ML 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 production or near-production deployment
  • Proficiency in Python and SQL; experience with frameworks such as XGBoost, LightGBM, PyTorch, or similar
  • Strong feature engineering skills - you know how to extract signal from messy, sparse, or heterogeneous financial data
  • Solid statistical foundation: anomaly detection, supervised classification, model calibration, experimentation design
  • Experience building and monitoring production models including alerting on performance degradation and concept drift
  • Familiarity with both rule-based and model-driven approaches - and when to use each
  • A point of view on how ML systems should be built - you can articulate tradeoffs, push back on bad decisions, and bring junior team members along
  • Comfort operating as a senior ML voice at an early-stage company - you own problems end-to-end and set the standard for others
  • Experience with emerging markets or data-sparse environments is a plus

We hire exceptional people from diverse backgrounds because different perspectives build better products.
If you're excited about this role but don't check every box, apply anyway. We value potential, ownership, and alignment with our values more than perfect résumés.
We are an equal opportunity employer and do not discriminate based on legally protected characteristics. We provide reasonable accommodations throughout the hiring process.
Compensation includes salary, equity, and benefits. Final offers are based on role scope, location, and experience.

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