At a Glance
- Tasks: Lead R&D in AI/ML for private credit, fine-tuning models and developing innovative workflows.
- Company: Join a forward-thinking firm at the forefront of financial technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Make a real impact by bridging cutting-edge research with practical applications in finance.
- Qualifications: 5+ years in data science, expertise in AI/ML, and strong problem-solving skills.
The predicted salary is between 70000 - 90000 £ per year.
We're looking for a Senior Data Scientist to lead R&D initiatives at the intersection of LLMs, information retrieval, and private credit analytics. You'll fine-tune small language models on financial documents, build agentic workflows for multi-step reasoning, and develop production-ready extraction systems that power our AI platform for institutional investors. This role bridges cutting‑edge research with real‑world deployment.
What You'll Do:
- Fine‑tune Small Language Models on proprietary private credit corpus (credit agreements, indentures, term sheets)
- Develop information retrieval systems: semantic search, ranking algorithms, and context‑aware retrieval
- Build agentic workflows with multi-step reasoning, tool use, reflection, and self‑correction capabilities
- Train classification models for document type identification, section detection, and entity recognition
- Create extraction models: NER for financial entities, relation extraction, structured table parsing
- Partner with Prompt Engineers on prompt optimization strategies and hybrid LLM+ML approaches
- Experiment with latest techniques: RAG architectures, fine‑tuning methods (LoRA, QLoRA), model distillation
- Present research findings to engineering team and stakeholders monthly (progress, insights, recommendations)
- Stay current with academic research and industry developments in NLP, LLMs, and financial ML
Production Readiness & Deployment:
- Write detailed technical specs for Platform team: model architecture, dependencies, deployment steps, API contracts
- Define production readiness criteria: performance benchmarks, edge case handling, failover mechanisms, rollback procedures
- Create comprehensive model cards: intended use, limitations, bias analysis, performance metrics, monitoring requirements
- Optimize models for production constraints: latency 95%, cost
Senior Data Scientist - AI/ML for Private Credit R&D employer: Alphastream
Join a forward-thinking company that values innovation and collaboration, where as a Senior Data Scientist, you'll have the opportunity to lead groundbreaking R&D initiatives in AI/ML for private credit. Our dynamic work culture fosters continuous learning and professional growth, supported by a team of experts dedicated to pushing the boundaries of technology. Located in a vibrant area, we offer competitive benefits and a unique environment that encourages creativity and impactful contributions to the financial sector.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist - AI/ML for Private Credit R&D
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at events. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.
✨Tip Number 2
Prepare for those interviews by brushing up on your technical skills and understanding the latest trends in AI/ML. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 3
Showcase your projects! Whether it's through a portfolio or GitHub, let your work speak for itself. We want to see how you’ve tackled real-world problems, especially in areas like financial ML and NLP.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Data Scientist - AI/ML for Private Credit R&D
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with AI/ML and data science in your application. We want to see how you've fine-tuned models or developed workflows in the past, so don’t hold back on those details!
Tailor Your Application:Customise your CV and cover letter to reflect the specific requirements of the Senior Data Scientist role. Use keywords from the job description to show us you understand what we're looking for.
Be Clear and Concise:When writing your application, keep it straightforward. We appreciate clarity, so avoid jargon unless it's necessary. Make it easy for us to see your qualifications and fit for the role.
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Alphastream
✨Know Your Models Inside Out
Make sure you’re well-versed in the small language models and techniques mentioned in the job description. Be ready to discuss your experience with fine-tuning models, especially in the context of financial documents. Prepare examples of how you've applied these skills in previous roles.
✨Showcase Your R&D Experience
This role is all about bridging research with real-world applications. Bring along specific projects where you’ve developed information retrieval systems or built workflows that demonstrate multi-step reasoning. Highlight any innovative solutions you've implemented and their impact.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions. Brush up on your knowledge of semantic search, ranking algorithms, and classification models. Be prepared to explain your thought process behind model architecture and deployment strategies, as well as how you handle performance benchmarks and edge cases.
✨Communicate Clearly and Confidently
You’ll need to present findings to both technical and non-technical stakeholders. Practice explaining complex concepts in simple terms. Use clear examples from your past work to illustrate your points, and don’t shy away from discussing challenges you faced and how you overcame them.
