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... in scripting languages (Python, PowerShell, shell) is a plus. * Industry certifications are a strong asset (e.g., GIAC, Microsoft SC-200, Splunk Enterprise Security, AWS/Azure security certs). Salary ...
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Python Salary In information
See Lenexa, KS salary details
$12.41 - $18.65
1% of jobs
$18.65 - $24.88
0% of jobs
$24.88 - $31.12
2% of jobs
$31.12 - $37.36
5% of jobs
$37.36 - $43.59
11% of jobs
$45.43 is the 25th percentile. Wages below this are outliers.
$43.59 - $49.83
18% of jobs
The median wage is $53.08 / hr.
$49.83 - $56.06
24% of jobs
$60.83 is the 75th percentile. Wages above this are outliers.
$56.06 - $62.30
18% of jobs
$62.30 - $68.54
13% of jobs
$68.54 - $74.77
5% of jobs
$74.77 - $81.01
3% of jobs
$12
$55
$81
How much do python salary in jobs pay per hour?
Which Python jobs pay the most?
What is the difference between Python Salary In vs Data Analyst Salary In?
| Aspect | Python Salary In | Data Analyst Salary In |
|---|---|---|
| Required Credentials | Proficiency in Python, data manipulation, and scripting skills | Proficiency in Excel, SQL, and data visualization tools |
| Work Environment | Tech companies, software firms, data-driven organizations | Business, finance, healthcare, and marketing sectors |
| Employer & Industry Usage | Used for automation, data analysis, and backend development | Used for reporting, data interpretation, and decision support |
Python Salary In typically involves programming and automation skills, often in tech-focused environments, while Data Analyst Salary In centers on data interpretation and reporting in various industries. Both roles require analytical skills but differ in technical depth and daily tasks.
Can I make 100k as a software developer?
Is Python a high paying job?
What pays more, C++ or Python?

Full-time
Posted 5 days ago
Job description
POSITION DESCRIPTION
Title: Senior Credit Risk/Decision Scientist
Classification: Salaried, exempt
Position Type: Full Time
Reports to: Credit Risk Officer – Digital Banking
Location: TBD
Summary/Objective
The Senior Credit Risk/Decision Scientist will be responsible for quantitative model development, credit strategy design, and analytical decision support throughout the customer life cycle. This role will build, validate, and monitor predictive models; design and interpret strategy tests; and translate analytical findings into actionable credit policy.
Essential Functions
Duties/Responsibilities:
• Develop, validate, and recalibrate credit risk scorecards and predictive models for acquisition, account management, and loss forecasting.
• Design and analyze champion-challenger tests to optimize credit policy and decisioning thresholds.
• Partner with Marketing to enhance response and bidding models focused on improved conversion and acquisition cost
• Monitor model performance through ongoing back-testing, stability analysis, and drift detection; recommend recalibration as needed.
• Integrate and evaluate third-party data vendors to enhance model features, leads waterfall and risk segmentation.
• Support prescreen modeling and strategies in partnership with marketing and credit strategy teams.
• Conduct portfolio-level risk analysis including delinquency trending, vintage analysis, and loss projections.
• Collaborate with compliance on model risk governance, fair lending review, and SR 11-7 documentation requirements.
• Prepare clear model documentation, validation reports, and executive-ready presentations for internal stakeholders and regulators.
• Partner with IT and data engineering teams on data pipelines, feature engineering, and model deployment in production environments.
• Contribute to fraud detection and collections analytics as workflow allows, supporting cross-functional risk initiatives.
Competencies:
• 5+ years of experience in credit risk modeling, decision science, or quantitative analytics within a bank, credit union, fintech, or consumer lender.
• Demonstrated experience building and validating scorecards using logistic regression, decision trees, gradient boosting, or similar techniques.
• Strong proficiency in Python or R for statistical modeling, data manipulation, and visualization
• Solid SQL skills; ability to independently access and analyze large datasets
• Familiarity with credit bureau data (Experian, Equifax, TransUnion) and alternative data sources.
• Understanding of model risk management frameworks, including SR 11-7 / OCC 2011-12 guidance.
• Strong analytical communication skills — ability to translate complex model outputs into actionable business recommendations.
• Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Finance, or a related quantitative field.