About our Firm:
LemFi (YC S21, Collection A) is a monetary expertise firm reshaping how the diaspora group in North America, Europe and the UK transfer their cash globally.
We construct services that permit our clients to ship, obtain, handle and do extra with their cash in a single app. We’re 1 Million Clients and extra robust, come be a part of us to assist construct the way forward for monetary providers for immigrants throughout the globe
Who you might be:
You’re a candidate who would thrive in a fintech startup atmosphere like ours, the place we readily settle for people with a humble, but uplifting perspective alongside a diligent sense of labor ethic. The groups right here at LemFi are captivated with their work and fields of experience, but in addition lend fingers on cross-functional duties to make sure the success of the corporate and the satisfaction of our clientele.
Job Abstract:
We’re searching for a extremely analytical and detail-oriented Lead Resolution Scientist to personal the event, deployment, and optimisation of credit score decisioning and danger fashions. You’ll play a central position in shaping our lending technique, constructing information merchandise, and driving portfolio efficiency via data-led perception.
This position is right for somebody with robust analytical and technical expertise who thrives on information exploration, modeling, and experimentation.
Key Tasks:
- Lead the event and upkeep of credit score danger and affordability fashions utilizing bureau, open banking, and different behavioural information.
- Personal end-to-end mannequin lifecycle: information sourcing, function engineering, mannequin improvement, validation, and monitoring.
- Design and execute champion/challenger assessments and A/B experiments to constantly enhance approval charges, loss charges, and buyer expertise
- Analyse credit score efficiency information to generate actionable perception and help strategic selections
- Mentor and develop a small group of analysts/information scientists because the group scales
- Work carefully with Knowledge Engineering to deploy fashions into manufacturing pipelines.
- Collaborate with stakeholders to outline modeling objectives and interpret mannequin outcomes in a enterprise context.
Necessities:
- 5-7 years of expertise in client credit score, notably in a knowledge science or determination science position.
- Fingers-on expertise constructing fashions in Python utilizing libraries like scikit-learn, XGBoost, or LightGBM.
- Sturdy expertise working with transactional datasets (e.g., Open Banking and Categorisation) and bureau information (e.g., Experian, Equifax).
- Deep understanding of function engineering, information preprocessing, and coping with class imbalance.
- Means to guage fashions utilizing acceptable metrics (e.g., AUC, KS, precision/recall) and validate throughout a number of segments.
- Familiarity with commonplace practices round mannequin monitoring, efficiency monitoring, and information drift.
- Sturdy SQL expertise for information extraction, becoming a member of, and transformation.
Most well-liked Expertise:
- Familiarity with unsupervised studying strategies reminiscent of Okay-means, DBSCAN, PCA, or autoencoders, and their software in credit score use circumstances like behavioral segmentation, fraud detection, or exploratory evaluation
- Expertise working in a start-up or scale-up atmosphere with quick decision-making cycles.
- Publicity to different information sources (e.g., system information, psychometric scoring) for credit score scoring.