Rothstein Recruitment
Senior Data Scientist - ML Engineer - Python - GenAI - Banking

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Senior Data Scientist - ML Engineer - Python - GenAI - Banking
Excellent opportunity opens to join an International Bank as their new Senior Data Scientist. You will take ownership of the Bank's data science strategy, and work with senior leadership to deliver the bank-wide data and AI roadmap.
Key Responsibilities
- Define and lead the data science strategy, feeding into the banks data and platform strategy.
- Leadership of data scientists within a cross functional team.
- Working with large and complex datasets to develop, test and deploy machine learning and AI models into production environments.
- Using MLOps practices to support model deployment, monitoring, governance, and continuous improvement.
- Evaluating emerging technologies and analytical techniques to identify opportunities for innovation and business value.
- Ensuring models and analytical solutions meet regulatory, governance, and risk management standards within a financial services environment.
- Reinforce Agile ways of working, using pair programming, DORA insights and best practices to help to foster an environment of continuous improvement within the team.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Qualifications & Experience
- Advanced Python and SQL skills, with experience using tools such as Jupyter/JupyterHub to conduct data exploration, develop machine learning solutions, and support production workflows.
- Experience in leading and design of enterprise wide data science strategy within financial services or similarly regulated industry.
- Experience with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), or other emerging AI technologies.
- Experience working with modern data platforms, software engineering best practices, and data science tooling to develop scalable and maintainable analytical solutions.
- Strong communication skills, with the ability to explain complex technical concepts and analytical findings to both technical and non-technical audiences.
- Strong practical knowledge of statistics, mathematics, and machine learning techniques, with experience developing, validating, deploying, and monitoring predictive and analytical models in production environments.
- Hands-on experience applying machine learning techniques using frameworks such as XGBoost, PyTorch, or similar technologies to solve complex business problems and deliver measurable value.
- Experience translating business requirements into actionable analytical solutions, working closely with stakeholders to identify opportunities, define approaches, and deliver data-driven outcomes.


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