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For Companies At a Glance
- Tasks: Shape the future of products using Python and advanced data techniques.
- Company: Dynamic startup focused on innovative financial solutions.
- Benefits: Competitive salary, flexible hours, and opportunities for professional growth.
- Other info: Join a team that values curiosity and creativity in tackling complex challenges.
- Why this job: Make a real impact by developing cutting-edge AI systems in a collaborative environment.
- Qualifications: Degree in Data Science or related field; experience with ML models and SQL.
The predicted salary is between 60000 - 80000 £ per year.
Requirements
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Demonstrated ability to design and validate features using transactional and behavioral data from financial services.
- Hands-on experience building and deploying ML models in Python.
- Proven track record in payment risk, fraud detection, or credit decisioning is strongly preferred.
- SQL skills and familiarity with AWS for deployment and data workflows.
- Experience with model governance and monitoring in regulated environments.
- Strong knowledge of statistical modeling, anomaly detection, clustering, and supervised/unsupervised learning.
- Hands-on experience with agentic AI frameworks (e.g. Claude with MCP, LangChain); experience in either transaction enrichment workflows or AI-assisted software development tooling is sufficient.
- Experience working with large-scale tabular data.
- Proven success collaborating with product and engineering teams to ship ML-based features and tools.
- Strong communication skills and business acumen to present complex technical ideas to non-technical stakeholders.
- Curious, proactive, and comfortable working in ambiguity in a scaling startup environment.
What the job involves
- You will use Python, with a strong grounding in feature engineering, model evaluation, and inference pipelines to help shape the future of our product offerings.
- Act as a subject matter expert in Analytics: Strong understanding of statistics, experimental design, and hypothesis testing to identify trends and patterns, and develop predictive models.
- Design and deploy Agentic AI systems capable of autonomous, multi-step reasoning, e.g. such as agents that categorise financial transactions by searching for merchant information, applying labels, and surfacing confidence scores with transparent reasoning.
- Lead data labeling at scale to produce ground-truth datasets and use ML techniques to maximise labelling efficiency.
- Build AI agents that integrate with external tools and data sources via protocols such as MCP, enabling LLMs to interact directly with codebases, APIs, or internal systems to automate complex workflows.
- Lead model deployment with an eye for performance, scalability, and real-time low-latency inference.
- Deliver robust, low-failure-rate models and systems, especially in environments where testing support is limited.
- Collaborative Problem Solving: Work closely with cross-functional teams, including product, engineering, and business stakeholders, to understand requirements and deliver data-driven solutions.
- Enthusiasm for collaborating across disciplines, including with academic researchers and third-party partners.
Data Scientist employer: Deepstreamtech
Contact Detail:
Deepstreamtech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML models and data analysis. We love seeing real-world applications of your work, so make sure to highlight your successes!
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the company’s products. We want to see how you can contribute, so be ready to discuss your experience with Python, SQL, and any relevant AI frameworks.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always looking for passionate candidates who are eager to join our team and help shape the future of data science.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in data science, especially with Python and ML models. We want to see how your skills match the job description, so don’t be shy about showcasing your achievements!
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 your background fits with our needs. We love seeing enthusiasm and a bit of personality!
Showcase Your Projects: If you've worked on any cool projects, especially those involving AI or financial data, make sure to mention them. We’re keen to see your hands-on experience and how you’ve tackled real-world problems.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Deepstreamtech
✨Know Your Data Science Fundamentals
Brush up on your statistics, experimental design, and hypothesis testing. Be ready to discuss how you've applied these concepts in real-world scenarios, especially in financial services. This will show that you can identify trends and develop predictive models effectively.
✨Showcase Your Technical Skills
Be prepared to talk about your hands-on experience with Python, SQL, and AWS. Highlight specific projects where you've built and deployed ML models, focusing on your feature engineering and model evaluation skills. This is your chance to demonstrate your technical prowess!
✨Communicate Clearly with Non-Technical Stakeholders
Practice explaining complex technical ideas in simple terms. You might be asked to present your work to non-technical team members, so being able to convey your insights clearly will set you apart. Think of examples where you've successfully communicated your findings.
✨Emphasise Collaboration and Problem Solving
Prepare examples of how you've worked with cross-functional teams to deliver data-driven solutions. Discuss any challenges you faced and how you overcame them collaboratively. This shows you're not just a lone wolf but a team player who thrives in a scaling startup environment.