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- Tasks: Join our Analytics team to design data-driven solutions for credit risk management.
- Company: Experian is a leading global information services company focused on innovation and diversity.
- Benefits: Enjoy hybrid working, great pay, healthcare, 25 days leave, and volunteering days.
- Why this job: Make a real impact in consumer lending while collaborating with clients and driving business success.
- Qualifications: Expertise in Python or SAS, with experience in credit risk modelling and stakeholder management.
- Other info: We celebrate diversity and are committed to creating an inclusive workplace for all.
The predicted salary is between 43200 - 72000 £ per year.
We have a new vacancy for an experienced Senior Data Science Consultant with coding expertise in Python or SAS to join our Analytics team, working with our cloud-based Ascend platform. You will partner with clients to understand their business, identify what data is required and how clients can best use Experian data models and analytics to improve business outcomes.
Responsibilities include:
- Design analytics solutions to client's problems in any area of consumer lending and credit risk management, using Experian analytics solutions.
- Engage in a consultative way with the client, to identify problems and define, design and deliver analytics solutions, with expertise in credit risk modelling and optimisation techniques.
- Present proposals to clients for analytics solutions, including recommendations.
- Provide consultancy on the potential 'bigger picture' strategies.
- Co-ordinate with Experian's Analytics Pre-Sales team to contribute to sales opportunities and support the conversion of sales prospects.
Experience and Skills:
- Data science experience with expertise in building decisioning or credit risk models using Python or SAS.
- Applied modelling and analytics experience to lead business decisions.
- Expertise in credit risk decisioning.
- Deep coding knowledge in Python with SAS or R.
- Good stakeholder management skills.
- Subject matter expert on the mechanics of consumer lending (risk, data usage, outcomes).
- Knowledge of Cloud / AWS.
- Product strategy experience desirable but not essential.
Benefits package includes:
- Hybrid working.
- Great compensation package.
- Core benefits include pension, Bupa healthcare, sharesave scheme and more.
- 25 days annual leave with 8 bank holidays and 3 volunteering days.
- You can purchase additional annual leave.
Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is a critical part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Senior Data Science Consultant - Credit Decisioning employer: Experian Group
Contact Detail:
Experian Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Science Consultant - Credit Decisioning
✨Tip Number 1
Familiarise yourself with the latest trends in credit risk modelling and decisioning. Being able to discuss recent developments or case studies during your interview will demonstrate your expertise and passion for the field.
✨Tip Number 2
Brush up on your coding skills, particularly in Python and SAS. Consider working on a small project or contributing to open-source projects that showcase your ability to build decisioning models, as this can be a great talking point in interviews.
✨Tip Number 3
Prepare to discuss how you would approach client consultations. Think about how you would identify their needs and design tailored analytics solutions, as this consultative approach is key to the role.
✨Tip Number 4
Network with professionals in the data science and credit risk fields. Engaging with industry peers can provide insights into the role and may even lead to referrals, increasing your chances of landing an interview.
We think you need these skills to ace Senior Data Science Consultant - Credit Decisioning
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data science, particularly with Python or SAS. Emphasise any relevant projects or roles that showcase your expertise in credit risk modelling and analytics.
Craft a Compelling Cover Letter: In your cover letter, explain why you are interested in the Senior Data Science Consultant position at Experian. Discuss how your skills align with their needs, particularly in designing analytics solutions and engaging with clients.
Showcase Relevant Experience: When detailing your work experience, focus on specific examples where you've successfully built decisioning or credit risk models. Highlight your consultative approach and any successful outcomes from your analytics solutions.
Prepare for Technical Questions: Be ready to discuss your coding skills in Python and SAS during the application process. Prepare examples of how you've used these languages in past projects, especially in relation to credit risk decisioning.
How to prepare for a job interview at Experian Group
✨Showcase Your Technical Skills
Make sure to highlight your coding expertise in Python or SAS during the interview. Be prepared to discuss specific projects where you've applied these skills, especially in building decisioning or credit risk models.
✨Understand the Client's Needs
Demonstrate your consultative approach by discussing how you would engage with clients to identify their problems. Prepare examples of how you've successfully designed analytics solutions tailored to client needs in the past.
✨Present with Confidence
Since presenting proposals is a key responsibility, practice articulating your ideas clearly and confidently. Use visuals or data to support your recommendations, and be ready to answer questions about your proposed solutions.
✨Familiarise Yourself with Cloud Technologies
Given the emphasis on cloud-based solutions, brush up on your knowledge of Cloud/AWS. Be ready to discuss how you can leverage these technologies to enhance analytics solutions for clients.
