Lead Decision Scientist
About our Company:
LemFi (YC S21, Series A) is a financial technology company reshaping how the diaspora community in North America, Europe and the United Kingdom move their money globally.
We build products and services that allow our customers to send, receive, manage and do more with their money in one app. We are 1 Million Customers and more strong, come join us to help build the future of financial services for immigrants across the globe
Who you are:
You are a candidate who would thrive in a fintech startup environment like ours, where we readily accept individuals with a humble, yet uplifting attitude alongside a diligent sense of work ethic. The teams here at LemFi are passionate about their work and fields of expertise, but also lend hands on cross-functional responsibilities to ensure the success of the company and the satisfaction of our clientele.
Job Summary:
We're seeking a highly analytical and detail-oriented Lead Decision Scientist to own the development, deployment, and optimisation of credit decisioning and risk models. You will play a central role in shaping our lending strategy, building data products, and driving portfolio performance through data-led insight.
This role is ideal for someone with strong analytical and technical skills who thrives on data exploration, modeling, and experimentation.
Key Responsibilities:
Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data.
Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring.
Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience
Analyse credit performance data to generate actionable insight and support strategic decisions
Mentor and develop a small team of analysts/data scientists as the team scales
Work closely with Data Engineering to deploy models into production pipelines.
Collaborate with stakeholders to define modeling goals and interpret model outcomes in a business context.
Requirements:
5-7 years of experience in consumer credit, particularly in a data science or decision science role.
Hands-on experience building models in Python using libraries like scikit-learn, XGBoost, or LightGBM.
Strong experience working with transactional datasets (e.g., Open Banking and Categorisation) and bureau data (e.g., Experian, Equifax).
Deep understanding of feature engineering, data preprocessing, and dealing with class imbalance.
Ability to evaluate models using appropriate metrics (e.g., AUC, KS, precision/recall) and validate across multiple segments.
Familiarity with standard practices around model monitoring, performance tracking, and data drift.
Strong SQL skills for data extraction, joining, and transformation.
Preferred Skills:
Familiarity with unsupervised learning methods such as K-means, DBSCAN, PCA, or autoencoders, and their application in credit use cases like behavioral segmentation, fraud detection, or exploratory analysis
Experience working in a start-up or scale-up environment with fast decision-making cycles.
Exposure to alternative data sources (e.g., device data, psychometric scoring) for credit scoring.
Apply Now
LemFi (YC S21, Series A) is a financial technology company reshaping how the diaspora community in North America, Europe and the United Kingdom move their money globally.
We build products and services that allow our customers to send, receive, manage and do more with their money in one app. We are 1 Million Customers and more strong, come join us to help build the future of financial services for immigrants across the globe
Who you are:
You are a candidate who would thrive in a fintech startup environment like ours, where we readily accept individuals with a humble, yet uplifting attitude alongside a diligent sense of work ethic. The teams here at LemFi are passionate about their work and fields of expertise, but also lend hands on cross-functional responsibilities to ensure the success of the company and the satisfaction of our clientele.
Job Summary:
We're seeking a highly analytical and detail-oriented Lead Decision Scientist to own the development, deployment, and optimisation of credit decisioning and risk models. You will play a central role in shaping our lending strategy, building data products, and driving portfolio performance through data-led insight.
This role is ideal for someone with strong analytical and technical skills who thrives on data exploration, modeling, and experimentation.
Key Responsibilities:
Lead the development and maintenance of credit risk and affordability models using bureau, open banking, and alternative behavioural data.
Own end-to-end model lifecycle: data sourcing, feature engineering, model development, validation, and monitoring.
Design and execute champion/challenger tests and A/B experiments to continuously improve approval rates, loss rates, and customer experience
Analyse credit performance data to generate actionable insight and support strategic decisions
Mentor and develop a small team of analysts/data scientists as the team scales
Work closely with Data Engineering to deploy models into production pipelines.
Collaborate with stakeholders to define modeling goals and interpret model outcomes in a business context.
Requirements:
5-7 years of experience in consumer credit, particularly in a data science or decision science role.
Hands-on experience building models in Python using libraries like scikit-learn, XGBoost, or LightGBM.
Strong experience working with transactional datasets (e.g., Open Banking and Categorisation) and bureau data (e.g., Experian, Equifax).
Deep understanding of feature engineering, data preprocessing, and dealing with class imbalance.
Ability to evaluate models using appropriate metrics (e.g., AUC, KS, precision/recall) and validate across multiple segments.
Familiarity with standard practices around model monitoring, performance tracking, and data drift.
Strong SQL skills for data extraction, joining, and transformation.
Preferred Skills:
Familiarity with unsupervised learning methods such as K-means, DBSCAN, PCA, or autoencoders, and their application in credit use cases like behavioral segmentation, fraud detection, or exploratory analysis
Experience working in a start-up or scale-up environment with fast decision-making cycles.
Exposure to alternative data sources (e.g., device data, psychometric scoring) for credit scoring.
Apply Now