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Credit risk analyst vs data scientist

The differences between credit risk analysts and data scientists can be seen in a few details. Each job has different responsibilities and duties. While it typically takes 1-2 years to become a credit risk analyst, becoming a data scientist takes usually requires 2-4 years. Additionally, a data scientist has an average salary of $106,104, which is higher than the $85,376 average annual salary of a credit risk analyst.

The top three skills for a credit risk analyst include risk management, SAS and SQL. The most important skills for a data scientist are python, data science, and visualization.

Credit risk analyst vs data scientist overview

Credit Risk AnalystData Scientist
Yearly salary$85,376$106,104
Hourly rate$41.05$51.01
Growth rate11%16%
Number of jobs32,578106,973
Job satisfaction--
Most common degreeBachelor's Degree, 70%Bachelor's Degree, 51%
Average age3941
Years of experience24

What does a credit risk analyst do?

A credit risk analyst's primary role is to assess loan and purchase applications to determine a client's ability to uphold financial obligations. Their responsibilities revolve around performing various analyzation techniques to evaluate financial risks, maintain records of all applications and relevant data, and provide advice on businesses on whether to approve or decline the credit application. Furthermore, a credit risk analyst may perform clerical tasks such as producing progress reports and presentations, responding to inquiries, and coordinating with all departments.

What does a data scientist do?

A Data Scientist analyzes information from multiple sources in order to gain maximum insight that can give the company a competitive advantage. They work in different domains, including manufacturing, healthcare, education, and finance.

Credit risk analyst vs data scientist salary

Credit risk analysts and data scientists have different pay scales, as shown below.

Credit Risk AnalystData Scientist
Average salary$85,376$106,104
Salary rangeBetween $62,000 And $116,000Between $75,000 And $148,000
Highest paying CityNew York, NYRichmond, CA
Highest paying stateNew YorkCalifornia
Best paying companyWestern Alliance BankThe Citadel
Best paying industryGovernmentStart-up

Differences between credit risk analyst and data scientist education

There are a few differences between a credit risk analyst and a data scientist in terms of educational background:

Credit Risk AnalystData Scientist
Most common degreeBachelor's Degree, 70%Bachelor's Degree, 51%
Most common majorFinanceComputer Science
Most common collegeUniversity of PennsylvaniaColumbia University in the City of New York

Credit risk analyst vs data scientist demographics

Here are the differences between credit risk analysts' and data scientists' demographics:

Credit Risk AnalystData Scientist
Average age3941
Gender ratioMale, 56.4% Female, 43.6%Male, 79.6% Female, 20.4%
Race ratioBlack or African American, 7.6% Unknown, 2.6% Hispanic or Latino, 9.5% Asian, 10.5% White, 69.4% American Indian and Alaska Native, 0.3%Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage11%9%

Differences between credit risk analyst and data scientist duties and responsibilities

Credit risk analyst example responsibilities.

  • Utilize data manipulation and quantitative analysis using VBA macros, SQL and advance excel knowledge to manage credit risk exposure.
  • Design and build portfolio management dashboard for senior management monthly credit strategy meetings using SAS.
  • Contribute significantly to credit portfolio analytics through integration of top-down macro risks with idiosyncratic issuer risks.
  • Perform monthly/quarterly operational functions supporting the SAS ETL processing to generate client profitability and performance measurement results.
  • Partner with municipal derivative marketing and trading risk associates to ensure seamless assimilation of individual trade characteristics.
  • Initiate behavior scorecard model for business strategy collection process by fitting logistic regression to longitudinal delinquency history data.
  • Show more

Data scientist example responsibilities.

  • Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
  • Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
  • Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
  • Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
  • Perform cross-validation-test on linear regression model of data using scikit-learn.
  • Develop python base statistical visualization to provide insights of fuzzy social media data.
  • Show more

Credit risk analyst vs data scientist skills

Common credit risk analyst skills
  • Risk Management, 14%
  • SAS, 9%
  • SQL, 9%
  • Strong Analytical, 5%
  • PowerPoint, 4%
  • Data Analysis, 4%
Common data scientist skills
  • Python, 13%
  • Data Science, 10%
  • Visualization, 5%
  • Java, 4%
  • Hadoop, 4%
  • Tableau, 3%

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