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For Companies At a Glance
- Tasks: Lead data science projects, design and deploy machine learning models for real-time customer engagement.
- Company: A cutting-edge AI organisation focused on personalised customer experiences.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Join a dynamic team and mentor fellow data scientists while engaging with clients.
- Why this job: Make a real impact in banking with advanced analytics and machine learning.
- Qualifications: Strong experience in data science, Python or R skills, and cloud platform knowledge.
The predicted salary is between 70000 - 90000 £ per year.
The Company
They are a specialist organisation working at the forefront of AI-driven customer engagement. Their focus is on enabling highly personalised, real-time decisions across digital channels using advanced analytics and machine learning.
The Role and Deliverables
- You will take ownership of end-to-end data science delivery, from problem definition through to deployment in live environments.
- Design, build, and deploy machine learning models within Pega-based decisioning platforms.
- Lead model development across feature engineering, training, validation, and ongoing performance monitoring.
- Develop statistical and machine learning models for personalisation, propensity scoring, and next-best-action use cases.
- Design and run experiments, including A/B testing, to quantify business impact.
- Provide technical leadership and mentoring to other data scientists, while engaging confidently with client stakeholders.
Your Skills and Experience
- Strong experience delivering data science solutions in client-facing or consulting environments.
- Advanced capability in Python and or R, with hands-on use of modern machine learning libraries.
- Experience deploying and operating models on cloud platforms such as AWS, GCP, or Azure.
- Experience with Pega Customer Decision Hub and Adaptive Decision Manager in production environments is highly desirable.
- Familiarity with MLOps practices and model monitoring frameworks is beneficial.
How to Apply
If you are looking for a senior data science role where you can combine technical depth with real client impact, apply now to learn more.
Lead Data Scientist - Banking employer: Harnham - Data & Analytics Recruitment
Contact Detail:
Harnham - Data & Analytics Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist - Banking
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science field, especially those who work in banking or AI. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving Python or R. 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 technical knowledge and soft skills. Be ready to discuss your experience with cloud platforms like AWS or GCP, and don’t forget to highlight your leadership abilities when mentoring others.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates who are eager to make an impact in the world of data science.
We think you need these skills to ace Lead Data Scientist - Banking
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Lead Data Scientist role. Highlight your experience with machine learning, Python, and any client-facing projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data science and how you can contribute to our mission of AI-driven customer engagement. Keep it engaging and relevant to the role.
Showcase Your Projects: If you've worked on any interesting data science projects, make sure to mention them! Whether it's deploying models on cloud platforms or conducting A/B tests, we love seeing real-world applications of your skills.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Harnham - Data & Analytics Recruitment
✨Know Your Data Science Stuff
Make sure you brush up on your data science fundamentals, especially around machine learning models and statistical techniques. Be ready to discuss your experience with Python or R, and how you've used them in real-world projects.
✨Showcase Your Client Engagement Skills
Since this role involves client-facing interactions, prepare examples of how you've successfully communicated complex data science concepts to non-technical stakeholders. Highlight any consulting experience you have, as it will show you're comfortable in that environment.
✨Demonstrate Your Technical Leadership
Think about times when you've mentored others or led a project. Be prepared to share specific examples of how you've guided junior data scientists or collaborated with teams to deliver successful outcomes.
✨Familiarise Yourself with Pega
If you have experience with Pega Customer Decision Hub or Adaptive Decision Manager, make sure to highlight it. If not, do some research on these platforms and be ready to discuss how you would approach deploying models in such environments.