At a Glance
- Tasks: Develop and enhance credit risk models using cutting-edge data science techniques.
- Company: Join Lendable, a leader in transparent consumer lending solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on continuous learning and innovation.
- Why this job: Make a real impact on consumer lending with innovative data-driven solutions.
- Qualifications: Proficiency in Python, SQL, and machine learning techniques; teamwork and communication skills are essential.
The predicted salary is between 50000 - 70000 £ per year.
Requirements
- Experience using Python and SQL
- Strong proficiency with data manipulation including packages like NumPy, Pandas
- Knowledge of machine learning techniques and their respective pros and cons
- Confident communicator and contributes effectively within a team environment
- Self-driven and willing to lead on projects / new initiatives (Desirable)
- Prior experience of credit risk for consumer lending or credit cards, especially for the US market (Desirable)
- Interest in machine learning engineering (Desirable)
- Strong SQL and interest in data engineering
What the job involves
- Learn the domain of products that Lendable serves, understanding the data that informs strategy and risk modelling is essential to being able to successfully contribute value
- Rigorously search for the best models that enhance underwriting quality
- Clearly communicate results to stakeholders through verbal and written communication
- Share ideas with the wider team, learn from and contribute to the body of knowledge
We are excited to be hiring a new Data Scientist into our team! 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. The data science team develops proprietary behavioural models combining state of the art techniques with a variety of data sources that inform market-facing underwriting and pricing decisions, scorecard development, and risk management. Data scientists work across the business in a multidisciplinary capacity to identify issues, translate business problems into data questions, analyse and propose solutions. We self-serve with all deployment and monitoring, without a separate machine learning engineering team. Design, implement, manage and evaluate experiments of products and services leading to constant innovation and improvement.
Data Scientist (US Products) employer: Lendable
Lendable is an exceptional employer, offering a dynamic work environment where data science plays a pivotal role in shaping innovative credit risk models for the US market. With a strong emphasis on employee growth, you will have access to cutting-edge machine learning techniques and a collaborative culture that encourages sharing ideas and learning from one another. Join us to be part of a forward-thinking team that values transparency, creativity, and continuous improvement in the financial services sector.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist (US Products)
✨Tip Number 1
Network like a pro! Reach out to current employees at Lendable or in the data science field on LinkedIn. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your Python and SQL projects, especially those involving machine learning. This will help you stand out and demonstrate your hands-on experience.
✨Tip Number 3
Practice your communication skills! Since you'll need to clearly convey complex data insights, try explaining your past projects to friends or family. The clearer you are, the better you'll perform in interviews.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining the Lendable team.
We think you need these skills to ace Data Scientist (US Products)
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with Python and SQL in your application. We want to see how you've used these tools in real-world scenarios, so don’t hold back on the details!
Communicate Clearly:Since we value confident communication, ensure your written application is clear and concise. Use straightforward language to explain your past experiences and how they relate to the role.
Tailor Your Application:Take a moment to tailor your application to our specific needs. Mention your interest in machine learning engineering and any relevant experience in credit risk for consumer lending or credit cards.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and get you into our system quickly!
How to prepare for a job interview at Lendable
✨Know Your Tech Inside Out
Make sure you're well-versed in Python and SQL, as these are crucial for the role. Brush up on data manipulation libraries like NumPy and Pandas, and be ready to discuss how you've used them in past projects.
✨Understand Machine Learning Fundamentals
Familiarise yourself with various machine learning techniques and their pros and cons. Be prepared to share examples of how you've applied these techniques in real-world scenarios, especially in credit risk modelling.
✨Communicate Clearly and Confidently
Since this role involves sharing insights with stakeholders, practice articulating your thoughts clearly. Think about how you can explain complex data findings in simple terms, and be ready to demonstrate your communication skills during the interview.
✨Show Your Initiative
Highlight any experience where you've taken the lead on projects or initiatives. Discuss how you approach problem-solving and innovation, especially in a team setting, to show that you're self-driven and collaborative.
