About the role
We are excited to be hiring a new Senior Data Scientist for our team! Ideally, this role will suit someone with a proven background in building models ideally in credit, lending, or other areas of financial services. Lendable is the market leader in real rate risk-based pricing, offering consumers transparency and product assurance at the point of application. Data Science sits at the heart of this USP, developing the credit risk models to underwrite loan and credit card products.
You will have access to the latest machine learning techniques combined with a rich data repository to deliver best in market risk models.
This role will primarily focus on our US unsecured loans and credit cards business.
Our team’s objectives
The data science team develops proprietary machine learning models combining state-of-the-art techniques with a variety of data sources that inform scorecard development and risk management, optimise marketing and pricing, and improve operations efficiency.
Research new data sources and unstructured data representation.
Data scientists work across the business in a multidisciplinary capacity to identify issues, translate business problems into data questions, analyse and propose solutions.
Deliver data services to a wide variety of stakeholders by engineering CLI programs / APIs.
Design, implement, manage and evaluate experiments of products and services leading to constant innovation and improvement.
How you’ll impact those objectives
Use your expertise to build and deploy models that contribute to the success of the business.
Stay up to date with the latest advancements in machine learning and credit risk modelling proactively proposing new approaches and projects that drive innovation.
Learn the domain of products that Lendable serves, understanding the data that informs strategy and risk modelling.
Extract, parse, clean and transform data for use in machine learning.
Clearly communicate results to stakeholders through verbal and written communication.
Mentor other data scientists and promote best practices throughout the team and business.
Key Skills
Knowledge of machine learning techniques and their respective pros and cons.
Ability to communicate sophisticated topics clearly and concisely.
Proficiency with creating ML models in Python with experiment tracking tools, such as MLFlow.
Curiosity, creativity, resourcefulness and a collaborative spirit.
Interest in problems related to the financial services domain - a knowledge of loan or credit card underwriting is advantageous.
Confident communicator and contributes effectively within a team environment.
Experience mentoring or leading others.
Self-driven and willing to lead on projects / new initiatives.
Familiarity with data used within credit risk decisioning such as Credit Bureau data, especially across multiple geographies is an advantage.
The interview process
We’re not corporate, so we try our best to get things moving as quickly as possible. For this role, we’d expect:
A quick phone call with the people team
Interview with hiring manager
Take home task
Task debrief
Case study interview
In person interview where you'll do your final round and have some lunch with the team
