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Credit Risk Data Science Jobs in Phoenix, AZ (NOW HIRING)

... reduce risk, and accelerate market readiness. The Main Responsibilities Build Data Strategy ... Bachelor's or Master's degree in Data Science, Statistics, Engineering, Computer Science, or ...

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Review financial statements to assess risk and assign appropriate credit ratings * Maintain and ... Advanced proficiency in Microsoft Excel (pivot tables, formulas, data analysis) * Ability to ...

Review financial statements to assess risk and assign appropriate credit ratings * Maintain and ... Advanced proficiency in Microsoft Excel (pivot tables, formulas, data analysis) * Ability to ...

Apply Early

Review financial statements to assess risk and assign appropriate credit ratings * Maintain and ... Advanced proficiency in Microsoft Excel (pivot tables, formulas, data analysis) * Ability to ...

Continuously monitor credit risk for a portfolio of accounts as new financial data, press releases, or other information becomes available throughout a company's lifecycle. * Recommend changes to ...

Continuously monitor credit risk for a portfolio of accounts as new financial data, press releases, or other information becomes available throughout a company's lifecycle. * Recommend changes to ...

Apply Early

... risk and compliance policies and procedures. What you have: * Bachelor's degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other ...

Data Scientist

Scottsdale, AZ · On-site

$80K - $120K/yr

Appointment optimization * Clinical risk stratification * Patient adherence forecasting ... Master's degree in Data Science, CS, Statistics, Biomedical Informatics, or related field preferred ...

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Credit Risk Data Science information

See Phoenix, AZ salary details

$36.7K

$113.1K

$196.1K

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

As of Jul 4, 2026, the average yearly pay for credit risk data science in Phoenix, AZ is $113,074.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,900.00 and $139,500.00 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 are the key skills and qualifications needed to thrive as a Credit Risk Data Scientist, and why are they important?

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 Phoenix, AZ? For Credit Risk Data Science jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Credit Risk Data Science jobs in Phoenix, AZ look for? The top searched job categories for Credit Risk Data Science jobs in Phoenix, AZ are:
Infographic showing various Credit Risk Data Science job openings in Phoenix, AZ as of June 2026, with employment types broken down into 1% As Needed, 76% Full Time, 19% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $113,074 per year, or $54.4 per hour.
Sr. Lead Data Product Manager

Sr. Lead Data Product Manager

Early Warning Services, LLC

Scottsdale, AZ • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 2 days ago


Job description

At Early Warning, we've powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.
Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment.
Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.
The Sr. Data Product Manager is a strategic role within the Certos Data & Analytics organization focused on enabling Data Science and Analytics teams through deep data management expertise. This leader drives the vision and requirements to scale and improve second- and third-party data, with an emphasis on quality, usability, and risk management. By translating stakeholder needs into actionable data, reporting, and analytics requirements, the role partners closely with data science, analytics, and technology teams to deliver high-quality, trusted data products that accelerate model development, insights generation, and compliant data use. This role will be critical in enabling our future state data science and ML Ops platform and support the requirements to build out capabilities.
Overall Purpose
The Sr. Lead Data Product Manager serves as the dedicated Data Product Manager for the Data Science organization, partnering closely with Data Scientists, Data Engineers, and ML Ops teams to enable adoption of the enterprise data and machine learning platform (Domino Data Lab).
his role will dive deeply into customer pain points, technology and security constraints, and market trends to develop a vision and requirements to scale second- and third-party data focusing on quality, usability, and risk management. They will translate stakeholder (internal/external) needs into technical data, reporting and analytics requirements, and partner across technology teams to facilitate the development of data products and capabilities.
Essential Functions:
  • Articulate vision, roadmap and technical requirements for data and analytics capabilities, processes and tools to enable prioritized use cases across the enterprise or business unit
  • Act as the primary liaison between Data Science and ML Ops teams, translating business and technical requirements into actionable platform capabilities, migration plans, and backlog priorities.
  • Lead requirements gathering, prioritization, and execution planning for the migration of data science workloads, models, and analytical processes to the enterprise ML Ops workbench.
  • Manage and prioritize a balanced product backlog that includes new features, technical debt, operational risks, platform enhancements, and defect remediation, ensuring alignment with Product-Oriented Thinking and Delivery (POTD) principles and business outcomes.
  • Partner with engineering and platform teams to identify, troubleshoot, and resolve data availability, data quality, and platform integration issues impacting data science and analytics use cases.
  • Define and monitor adoption, performance, and value realization metrics associated with data science platform modernization and migration efforts.
  • Advocate for data management best practices, governance requirements, and operational excellence to ensure trusted, scalable, and compliant use of enterprise data assets.
  • Develop and maintain the data product roadmap; scopes and prioritizes activities based on the CDO, business, and user impact which defines product enhancements for both short-term and long-term.
  • Gain a deep understanding of end-user needs and experience (internal data consumers), identify and fill product gaps and generate new ideas that grow adoption and improve user experience
  • Define requirements for the Data Technology team on data processing and storage to facilitate a variety of analytics use cases such as effective analysis and reporting, data science exploration and model development, and data quality and anomaly detection
  • Set and achieve success metrics to meaningfully improve business results, focusing on what success looks like for our internal data consumers and how accessible data and insights could help them deliver enterprise value
  • Act as a product evangelist to build awareness, understanding, adoption, and buy-in
  • Support the company's commitment to risk management and protect the integrity and confidentiality of systems and data. Oversee the integration of strong data governance, risk and security controls.

Minimum Qualifications
  • Education and/or experience typically obtained through completion of a bachelor's degree in STEM or related field
  • 9+ years' experience or related experience in product management, data management, or consulting with a proven record of high performance, preferably with experience building data and analytics products
  • 5+ years' experience with direct hands-on responsibility working with data or data teams (data engineers, data scientists, software developers, data analysts, etc.), business stakeholders and end-users
  • Strong business intuition and the technical ability to understand, design, and explain complex product and data strategies to both business and technical audiences
  • Proficiency with software development methodologies such as Agile and experience working with Scrum teams and working with Agile tools such as Jira
  • Strong project and stakeholder management with the ability to work effectively with cross functional teams with diverse skill sets across all levels of the organization
  • Excellent communications skills, both oral and written
  • Hands-on experience writing and tuning SQL
  • Comfort with product analytics and data visualization tools (e.g. Tableau and Mixpanel)
  • Background and drug screen

Preferred Qualifications
  • Master's degree in STEM or related field
  • Experience supporting Data Science, Machine Learning, or Advanced Analytics teams through platform modernization, cloud transformation, or ML Ops implementation initiatives.
  • Strong data management background, including hands-on experience with data sourcing, data lineage, metadata, data quality, and data governance concepts.
  • Demonstrated ability to analyze data issues, write complex queries, and work directly with engineering teams to troubleshoot data pipelines, integrations, and platform-related challenges.
  • Experience managing product backlogs that balance business value, technical debt, operational risk, platform stability, and regulatory requirements.
  • Familiarity with modern data science and ML Ops platforms such as Domino Data Lab, Databricks, SageMaker, or similar environments.
  • Working knowledge of model development lifecycle, feature engineering, model deployment, and ML governance processes.
  • Experience working with terabyte size datasets
  • Understanding of data science tools and concepts and experience directly enabling data science teams or delivering data science use cases
  • Experience with: Salesforce, Google Analytics, SQL Server, Hive, Spark, SQL, and data architecture tools such as HDFS, Aerospike, ElasticSearch, Kafka
  • Deep understanding of best practices and unique data and analytics requirements across functional areas such Product, Marketing, Data Science, Finance, Operations

Employee must be able to perform essential functions and physical requirements of position with or without reasonable accommodation.
Physical Requirements
Working conditions consist of a normal office environment. Work is primarily sedentary and requires extensive use of a computer and involves sitting for periods of approximately four hours. Work may require occasional standing, walking, kneeling, and reaching. Must be able to lift 10 pounds occasionally and/or negligible amount of force frequently. Requires visual acuity and dexterity to view, prepare, and manipulate documents and office equipment including personal computers. Requires the ability to communicate with internal and/or external customers.
The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow instructions and perform other related duties as assigned by their supervisor.
The base pay scale for this position in:
Phoenix, AZ in USD per year is: $150,000 - $200,000.
San Francisco, CA in USD per year is: $180,000 - $240,000.
Additionally, candidates are eligible for a discretionary incentive plan and benefits.
This pay scale is subject to change and is not necessarily reflective of actual compensation that may be earned, nor a promise of any specific pay for any specific candidate, which is always dependent on legitimate factors considered at the time of job offer. Early Warning Services takes into consideration a variety of factors when determining a competitive salary offer, including, but not limited to, the job scope, market rates and geographic location of a position, candidate's education, experience, training, and specialized skills or certification(s) in relation to the job requirements and compared with internal equity (peers). The business actively supports and reviews wage equity to ensure that pay decisions are not based on gender, race, national origin, or any other protected classes.
Some of the Ways We Prioritize Your Health and Happiness
  • Healthcare Coverage - Competitive medical (PPO/HDHP), dental, and vision plans as well as company contributions to your Health Savings Account (HSA) or pre-tax savings through flexible spending accounts (FSA) for commuting, health & dependent care expenses.
  • 401(k) Retirement Plan - Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.
  • Paid Time Off - Flexible Time Off for Exempt (salaried) employees, as well as generous PTO for Non-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunteer day.
  • 12 weeks of Paid Parental Leave
  • Maven Family Planning - provides support through your Parenting journey including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.

And SO much more! We continue to enhance our program, so be sure to check our Benefits page here for the latest. Our team can share more during the interview process!
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Early Warning Services, LLC ("Early Warning") considers for employment, hires, retains and promotes qualified candidates on the basis of ability, potential, and valid qualifications without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote equal employment opportunity and affirmative action, in accordance with all applicable federal, state, and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our employees.