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Credit Risk Data Science Jobs in Tennessee (NOW HIRING)

Symicor Group

Sr. Credit Officer

Brentwood, TN · On-site

$270K/yr

Developing department financial goals including credit quality, volume, and risk distribution ... Reviewing real estate data and assessing the impact on the Bank's loan portfolio. * Delegating ...

Cypress HCM

Credit Analyst

Nashville, TN · On-site

$20 - $22.76/hr

... manage risk and support financial stability. The Credit Analyst's expertise will contribute to ... Partner with Data Governance providing direction on Customer Set ups * Serve as back up for ...

Cyod

Principal Data Scientist

Shelbyville, TN · On-site

$140 - $180/hr

Experience: 10+ years of experience in data science, machine learning, healthcare analytics ... Understanding of healthcare data sensitivity, PHI protection, explainability, model risk, clinical ...

The Symicor Group

Sr. Credit Officer

Brentwood, TN · On-site

$270K/yr

Developing department financial goals including credit quality, volume, and risk distribution ... Reviewing real estate data and assessing the impact on the Bank's loan portfolio. * Delegating ...

CFS

Credit Screening Representative

Goodlettsville, TN · On-site

$18 - $20/hr

Conduct comprehensive credit risk assessments to support informed lending decisions, ensuring ... Light Excel skills - Data Entry, Spreadsheets * Desire to meet key phone metrics. * Willingness to ...

Nutrien

Credit Manager

Collierville, TN · On-site

... risk, and approve or reject accounts completing analysis summaries * Direct all credit and ... data processing, personnel, and other departments concerned with administration and credit and ...

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Showing results 21-40

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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 Tennessee?

For Credit Risk Data Science jobs in Tennessee, the most frequently searched job titles are:

What job categories do people searching Credit Risk Data Science jobs in Tennessee look for?

The top searched job categories for Credit Risk Data Science jobs in Tennessee are:

What cities in Tennessee are hiring for Credit Risk Data Science jobs?

Cities in Tennessee 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 Tennessee as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, and 5% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

Assistant Professor of Computer Science / Data Science

FISK UNIVERSITY

Nashville, TN

Full-time

Re-posted 20 days ago

Job description

POSITION SUMMARY:

Fisk University Assistant Professor position in Computer Science/Data Science (Tenure-Track or Tenured) provides an excellent opportunity to establish a rigorous academic career based on curriculum innovation, scholarly work and academic service. The individual should ensure that students meet high academic standards while providing a nurturing and learner-centered environment in the classroom, laboratory or research setting, and via diverse modalities of instructional delivery. The individual has demonstrated experience and expertise in curriculum innovation, interdisciplinary studies and innovative teaching strategies. Teaching and scholarly excellence in the areas of speciality will be a high priority. Additionally, the individual is strongly urged to integrate instruction with research and community service. The individual should be able to work productively with students and colleagues of diverse ethnic and cultural backgrounds. The service on University committees and contribution to the development of the University’s Mission and Vision is required. As Fisk University is in the booming downtown of Nashville, TN, there are collaborative opportunities with local high-tech industries. The department faculty members have successfully obtained several federally funded research grants (NSF/NIH) and the individual will have opportunities to establish a strong federally funded research program.

ESSENTIAL FUNCTIONS:

Primary responsibilities include teaching introductory and upper-division courses (12 credit hours per semester; and helping to develop and expand programs in Computer Science/Data Science. Excellence in teaching at the undergraduate level is essential. Understanding, support, and willingness to participate in the advancement of the liberal arts is required. An active research agenda that is open to participation of undergraduate and/or graduate students is expected. A Ph.D. in Computer Science, or a closely related field, is required.

SECONDARY FUNCTIONS:

Additional expectations of an Assistant Professor include participation in the Faculty Assembly, University service as an academic advisor or a student campus organization advisor and engagement in activities that generate professional development leading to demonstrated achievement and other mandatory campus events. The individual should express a zeal for and competence in grantsmanship and be supportive of the University’s thrust to increase sponsored programs and outside funding.

SPECIFIC JOB KNOWLEDGE, SKILLS AND ABILITIES:

Extensive knowledge in Computer Science, Data Science or Information Technology. Proven excellence in teaching at the college level, genuine interest in teaching, collegiate behavior with peers and a sincere interest to extract the best from our students are important attributes of persons holding the position.

ORGANIZATIONAL RESPONSIBILITY:

None

A doctorate degree in Computer Science, Computer Engineering, Information Technology, Data Science or relevant areas. The potential for excellence in teaching and research.

Education: A doctorate degree in computer science or relevant areas.

Experience: None

Licenses or Certificates: None

Additional Requirements: A background and/or credit check will be required of the successful applicant. An official transcript (if applicable) and names, addresses and phone numbers of three (3) references will be required of the successful applicant.

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