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- Tasks: Lead data science projects in the banking sector, utilising advanced analytics and machine learning.
- Company: Join a global data science team expanding across Europe, with a focus on financial services.
- Benefits: Enjoy remote work flexibility and a competitive salary of £85,000.
- Why this job: Be part of an inclusive culture that values diversity and drives impactful client solutions.
- Qualifications: 8+ years of experience in data science, preferably in financial services, with expertise in Python and ML frameworks.
- Other info: Fluency in French or German is a plus for future client interactions.
The predicted salary is between 68000 - 102000 £ per year.
Senior Data Scientist
Salary: £85,000
UK-Based (Remote)
I’m working with a global, growing data science capability which works across multiple markets. The team is distributed, and they are building on regional depth in Europe, with recent expansion into France and potential future moves into Germany.
I’m looking for a seasoned data scientist (8+ years) with strong domain expertise in financial services, ideally someone who has worked across both B2B client-facing engagements.
Languages & Libraries:
- Python
- R
- Pandas
- NumPy
- scikit-learn
- XGBoost
- LightGBM
- statsmodels
NLP & ML Frameworks:
- spaCy
- Hugging Face Transformers
- TensorFlow or PyTorch (where applicable)
Data Engineering & Pipelines:
- dbt
- Airflow
- SQL
- Spark
- Dask
Fluency in French or German is a strong bonus as it will be advantageous for future client engagements.
SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.
Lead Data Scientist - Banking employer: SPG Resourcing
Contact Detail:
SPG Resourcing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist - Banking
✨Tip Number 1
Make sure to showcase your experience in financial services during networking events or discussions. Engaging with professionals in the banking sector can help you gain insights and potentially lead to referrals.
✨Tip Number 2
Familiarise yourself with the latest trends in data science, particularly in the banking industry. Being able to discuss recent advancements or case studies can set you apart in conversations with potential employers.
✨Tip Number 3
If you speak French or German, highlight this skill in your interactions. It could be a significant advantage for client-facing roles, so don’t hesitate to mention it when networking or during interviews.
✨Tip Number 4
Join relevant online communities or forums where data scientists in banking share knowledge and opportunities. Engaging in these spaces can help you stay informed and connect with others who might know about job openings.
We think you need these skills to ace Lead Data Scientist - Banking
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your 8+ years of experience in data science, particularly in financial services. Emphasise your expertise with the required languages and libraries such as Python, R, and machine learning frameworks.
Craft a Compelling Cover Letter: Write a cover letter that showcases your domain expertise in banking and your experience with B2B client-facing engagements. Mention any relevant projects or achievements that demonstrate your skills and how they align with the company's goals.
Highlight Language Skills: If you are fluent in French or German, make sure to mention this prominently in your application. This could set you apart from other candidates and show your potential value for future client engagements.
Showcase Your Technical Skills: In your application, provide specific examples of how you've used tools like dbt, Airflow, SQL, and Spark in your previous roles. This will demonstrate your hands-on experience and technical proficiency, which is crucial for the role.
How to prepare for a job interview at SPG Resourcing
✨Showcase Your Domain Expertise
Make sure to highlight your experience in financial services during the interview. Discuss specific projects where you've applied data science techniques to solve real-world problems in banking or finance.
✨Demonstrate Technical Proficiency
Be prepared to discuss your proficiency in Python, R, and relevant libraries like Pandas and NumPy. You might be asked to solve a technical problem on the spot, so brush up on your coding skills and be ready to explain your thought process.
✨Familiarise Yourself with NLP & ML Frameworks
Since the role involves working with NLP and machine learning frameworks, review your knowledge of tools like spaCy and TensorFlow. Be ready to discuss how you've used these technologies in past projects and their impact on your results.
✨Language Skills Matter
If you speak French or German, make sure to mention it! This could set you apart from other candidates, especially since the company is expanding into those markets. Practice discussing your language skills and any relevant experiences you have in client-facing roles.
