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For Companies At a Glance
- Tasks: Develop ML models, analyze data, and mentor junior team members.
- Company: Join a cutting-edge FinTech transforming payment solutions globally.
- Benefits: Work onsite in London with a dynamic team and innovative projects.
- Why this job: Make an impact in the payments landscape using advanced data science techniques.
- Qualifications: Bachelor’s, Master’s, or PhD in relevant fields; experience in data science required.
- Other info: Opportunity to work with cloud platforms and contribute to ethical AI practices.
The predicted salary is between 43200 - 72000 £ per year.
Senior Data Scientist — Onsite A FinTech specialising in cloud based payment solutions is looking for an experienced Senior Data Scientist to join their growing team in London. This innovative organisation is transforming the payments landscape by delivering seamless, data driven solutions for businesses worldwide. This is an opportunity to work on impactful projects, leveraging advanced data science techniques to optimise payment processing, detect anomalies, and improve operational efficiency. If you’re passionate about applying your skills in data science to drive innovation in payments, this role is for you. What You’ll Be Doing Developing ML models to optimise payment workflows, detect anomalies, and predict payment success rates. Applying graph analytics techniques to uncover hidden relationships in transaction data and improve fraud detection mechanisms. Building and maintaining scalable data pipelines to support real time and batch processing of payment data. Leveraging Spark for distributed data processing and ML on large datasets. Collaborating with cross functional teams, including product, engineering, and operations, to translate business needs into data-driven solutions. Conducting deep statistical analysis and modelling to generate actionable insights for improving the payment platform. Mentoring junior team members and championing best practices in data science, including explainable AI and ethical model design. What They’re Looking For Education: Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Mathematics, Physics, or a related field. Experience: Professional experience in data science and graph analytics, preferably in the financial or payments industry. Expertise: Strong understanding of graph databases, graph algorithms, and graph analytics techniques. Technical Skills: Proficiency in programming languages such as Python or R, with experience using Spark for large-scale data processing and machine learning. Communication: Excellent communication skills with the ability to explain complex models to technical and non technical stakeholders alike. Collaboration: Proven ability to work collaboratively in both onsite and remote environments, contributing effectively to a distributed team. Desirable Skills Experience with cloud platforms such as AWS or GCP. Familiarity with transaction data processing and anomaly detection techniques. Knowledge of deploying models into production using MLOps tools like MLflow or SageMaker. intro Consulting Ltd are proud to represent this forward thinking FinTech client. We act as their trusted recruitment partner, connecting top talent with meaningful opportunities.
Senior Data Scientist employer: intro
Contact Detail:
intro Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist
✨Tip Number 1
Make sure to showcase your experience with machine learning models and graph analytics in your conversations. Highlight specific projects where you've optimized workflows or detected anomalies, as this will resonate well with the hiring team.
✨Tip Number 2
Familiarize yourself with the latest trends in payment processing and fraud detection. Being able to discuss current challenges and innovations in the FinTech space will demonstrate your passion and knowledge during interviews.
✨Tip Number 3
Prepare to discuss your experience with cloud platforms like AWS or GCP. If you have worked on deploying models using MLOps tools, be ready to share those experiences, as they are highly relevant to the role.
✨Tip Number 4
Emphasize your collaboration skills by sharing examples of how you've worked with cross-functional teams. This will show that you can effectively translate business needs into data-driven solutions, which is crucial for this position.
We think you need these skills to ace Senior Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science and graph analytics, especially within the financial or payments industry. Emphasize your proficiency in Python or R and any experience with Spark.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data science and how it can drive innovation in payments. Mention specific projects or experiences that demonstrate your ability to develop ML models and conduct statistical analysis.
Showcase Collaboration Skills: Highlight examples of how you've successfully collaborated with cross-functional teams. This could include working with product, engineering, or operations teams to translate business needs into data-driven solutions.
Prepare for Technical Questions: Be ready to discuss your technical skills in detail, particularly your experience with graph databases and anomaly detection techniques. Prepare to explain complex models in a way that is understandable to both technical and non-technical stakeholders.
How to prepare for a job interview at intro
✨Showcase Your Technical Skills
Be prepared to discuss your experience with programming languages like Python or R, and highlight any projects where you've used Spark for large-scale data processing. They’ll want to see how you can apply these skills in real-world scenarios.
✨Demonstrate Your Understanding of Graph Analytics
Since the role emphasizes graph analytics, make sure to explain your familiarity with graph databases and algorithms. Prepare examples of how you've used these techniques to uncover relationships in data or improve fraud detection.
✨Communicate Complex Ideas Clearly
Practice explaining complex models and data science concepts in simple terms. This is crucial as you'll need to communicate effectively with both technical and non-technical stakeholders.
✨Highlight Collaboration Experience
Discuss your experience working in cross-functional teams. Provide examples of how you've collaborated with product, engineering, and operations teams to translate business needs into data-driven solutions.
