Freelance Credit Risk Data Scientist – Machine Learning for Lending Products

Company Description

ThreatXIntel is a startup cyber security company dedicated to protecting businesses and organizations from evolving cyber threats. We specialize in providing tailored, cost-effective solutions in areas such as cloud security, web and mobile security testing, DevSecOps, and more. Our proactive approach ensures continuous monitoring and testing to identify vulnerabilities before they can be exploited. With a mission to deliver high-quality services, ThreatXIntel empowers businesses of all sizes to safeguard their digital assets effectively. Our team fosters peace of mind, allowing clients to focus on achieving their growth goals.

Role Description

We are seeking an experienced

Credit Risk Data Scientist

to design and implement advanced machine learning models for predicting

credit risk

across multiple

short-term lending products , including tax refund advances,

Buy Now Pay Later (BNPL) , and

installment loans . This is a hands-on, data-driven role requiring both technical depth and business acumen to drive impactful lending decisions.

Key Responsibilities

• Design, build, evaluate, and defend

machine learning models

to predict

credit risk

across various lending products

product

and

risk teams

to align model outcomes with business goals and lending strategies

data pipelines

for feature engineering, model training, scoring, and performance tracking using

Python

and

SQL

production environments , collaborating with

engineering teams

and other data scientists to ensure scalability and performance

credit policies

driven by insights from machine learning models

AUC, KS, Gini , and stability measures like

PSI

and

CSI

model fairness ,

interpretability , and compliance with

regulatory frameworks

(e.g.,

FCRA ,

ECOA )

data privacy

and

ethical considerations

related to credit risk models

Required Skills and Experience

Bachelor’s or Master’s degree

in

Mathematics ,

Statistics ,

Computer Science , or a related field

2+ years

of professional experience in

Data Science

or

Machine Learning , with a focus on

credit risk modeling

Python

libraries (e.g.,

scikit-learn ,

XGBoost ,

LightGBM ,

pandas ,

numpy )

SQL

for data extraction, transformation, and analysis

tree-based models ,

regression ,

time series ,

causal inference , and

clustering

techniques

large-scale datasets ,

feature engineering , and

model training

Experience in credit risk modeling

or in the

fintech domain

PD calibration ,

reject inference ,

adverse action logic , and

risk segmentation

credit bureau data

(e.g.,

TransUnion ,

Experian ,

Equifax ) and

alternative credit data sources

like

cash flow data

communication ,

collaboration , and

problem-solving

skills to work with business and technical teams

Preferred Experience

short-term lending ,

BNPL , or

tax advance products

credit risk model governance

and

regulatory compliance

automated model governance frameworks

for monitoring and ensuring compliance in credit risk models