At a Glance
- Tasks: Build and deploy cutting-edge machine learning models for credit risk in financial services.
- Company: Join a dynamic team at the forefront of data science in the financial sector.
- Benefits: Competitive salary, mentorship opportunities, and access to the latest tech.
- Other info: Collaborative environment with opportunities for professional growth and innovation.
- Why this job: Make a real impact on financial products while innovating with advanced data techniques.
- Qualifications: Experience in machine learning, Python, and a passion for financial services.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- Proven background in building models, ideally in credit, lending, or other areas of financial services.
- 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; 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.
What the job involves
- Hiring a new Senior Data Scientist for our team.
- Data Science sits at the heart of this USP, developing the credit risk models to underwrite loan and credit card products.
- 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 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.
- 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.
- 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.
Senior Data Scientist (US Products) employer: Deepstreamtech
At Lendable, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a Senior Data Scientist, you will have the opportunity to work with cutting-edge machine learning techniques in the financial services sector, while also benefiting from a supportive environment that encourages professional growth and mentorship. Our commitment to employee development, combined with access to a rich data repository, makes Lendable a unique place to advance your career in data science.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist (US Products)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other data scientists. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to credit risk or financial services. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your communication skills. Practice explaining complex topics in simple terms, as this is key in our field. Remember, it’s not just about what you know, but how you share that knowledge!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Senior Data Scientist (US Products)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Senior Data Scientist role. Highlight your background in building models, especially in credit or lending, and don’t forget to mention your proficiency in Python and machine learning techniques.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about financial services and how your curiosity and creativity can contribute to our team. Keep it clear and concise, just like we do with our data!
Showcase Your Communication Skills:As a confident communicator, it’s important to demonstrate your ability to explain complex topics simply. In your application, include examples of how you've effectively communicated data insights to stakeholders or mentored others in your previous roles.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Deepstreamtech
✨Know Your Models Inside Out
Make sure you can discuss the machine learning models you've built in detail. Be ready to explain the pros and cons of different techniques, especially in the context of credit and lending. This shows your depth of knowledge and helps you stand out.
✨Communicate Clearly
Practice explaining complex data science concepts in simple terms. You’ll need to communicate effectively with stakeholders who may not have a technical background. Use examples from your past work to illustrate your points.
✨Show Your Curiosity
Demonstrate your interest in financial services by discussing recent trends or challenges in the industry. Bring up any innovative ideas you have for using data in credit risk modelling. This will show that you're proactive and engaged.
✨Be a Team Player
Highlight your experience mentoring others and collaborating on projects. Share specific examples of how you've contributed to team success in the past. This will show that you’re not just a great data scientist, but also a valuable team member.
