ML-Driven Credit Optimization Specialist

humani; Kentucky; On-site; USD 41,000 - 49,000; Full time

Benefits offered by this job

Bonus

Private medical insurance

Ticket restaurants

Job summary

humani is seeking a hands-on Machine Learning Engineer to map credit optimization and predictive capabilities of ML systems using customer, transactional, financial and behavioural data.

The role will develop predictive and optimization models to improve credit decisions across a large FMCG environment, balancing customer value, exposure and risk, with emphasis on turning insights into practical credit recommendations.

Qualifications

• Hands-on experience in credit analytics and credit optimization.
• Strong practical capability in ML, data science, and statistical modelling.
• Experience with high-volume customer-credit environments such as FMCG/retail/finance.

Responsibilities

• Develop and continuously improve credit scoring, behavioural and predictive models.
• Build models to support credit-limit and exposure optimisation.
• Analyse customer payment behaviour and develop early-warning indicators.
• Apply ML and advanced analytics to collections and recovery optimisation.
• Segment customers according to credit behaviour, risk and value.
• Translate predictive models into practical credit recommendations and rules.
• Monitor, validate and continually improve model performance.
• Collaborate with Credit, Finance, Commercial and Data/Technology teams to embed analytical solutions.
• Identify opportunities where better credit intelligence can support commercial growth while reducing risk.

Skills

Credit Analytics / Credit Optimization

Machine Learning

Data Science

Python

R

SQL

Predictive Modelling

Statistical Modelling

Optimization Techniques

Job description

humani is seeking a hands-on Machine Learning Engineer to map credit optimization and predictive capabilities of ML systems using customer, transactional, financial and behavioural data.

The role will develop predictive and optimization models to improve credit decisions across a large FMCG environment, balancing customer value, exposure and risk, with emphasis on turning insights into practical credit recommendations.