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- Tasks: Design, build, and deploy machine learning solutions in a collaborative environment.
- Company: Join Liberis, a scaling FinTech organization pushing AI boundaries.
- Benefits: Enjoy autonomy, collaboration, and direct access to decision makers in our London office.
- Why this job: Make an impact with cutting-edge technology while working with a talented team.
- Qualifications: Hands-on ML experience, strong Python skills, and expertise in forecasting or risk-based modeling required.
- Other info: Flexible application process; reach out even if you don't meet every requirement!
The predicted salary is between 43200 - 72000 £ per year.
Joining a multi-skilled team of Data Scientists & Engineers, you will utilise your Data Science skills to design, build and deploy machine learning solutions . Our tech stack is based in GCP and our Engineering & Product Teams are co-located in our London office – ensuring you will have direct access to decision makers and colleagues to drive projects forward quickly, with collaboration playing a key role.
Our Teams are empowered to push the boundaries of the impact AI can have within a scaling, multi-product FinTech organisation – with autonomy and independence in abundance at Liberis. Your role will be responsible for developing scalable ML systems while collaborating with peers and contributing independently to the success of our projects.
What you’ll get to do:
- Design, develop, and deploy end-to-end machine learning systems in Python, ensuring reliability, scalability, and performance.
- Collaborate with data scientists and engineers to integrate machine learning models into production systems, focusing on the quality and maintainability of solutions.
- Work independently to address technical challenges in machine learning pipelines and model deployment.
- Collaborate closely with cross-functional teams and communicate technical concepts effectively to both technical and non-technical stakeholders.
Interview process:
- Screening call with Chess (Internal recruiter)
- Video interview with the Hiring Manager
- Tech interview with the ML & AI Team (project discussion)
- Tech interview with the ML & AI Team (skills discussion)
- Interview with the Engineering Manager
What you’ll bring:
- Hands-on experience in an ML engineering role, with a track record of developing and deploying machine learning models in production.
- Strong expertise in Python, including data analysis libraries such as Pandas and Numpy, and machine learning frameworks like PyTorch or TensorFlow.
- Candidates must have experience in either forecasting (e.g., revenue forecasting, time series modeling) or risk-based modeling (e.g., Probability of Default, credit risk metrics), as this role requires expertise in at least one of these critical areas to drive data-driven decisions.
- Deep understanding of machine learning concepts, including optimisation, statistics, and algorithm development.
- Experience in building and maintaining cloud-based machine learning services, preferably using GCP or other cloud platforms.
- Solid understanding of classical ML algorithms (e.g., Logistic Regression, Random Forest, XGBoost) and modern deep learning techniques (e.g., LSTM).
Next Steps:
If this opportunity feels like the right fit for your next career move, we’d love to hear from you! Even if you don’t meet every requirement, don’t hesitate to apply or reach out to Chess (Internal Recruiter) at .
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Senior Data Scientist employer: Liberis Limited
Contact Detail:
Liberis Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist
✨Tip Number 1
Familiarize yourself with the specific machine learning frameworks mentioned in the job description, like PyTorch or TensorFlow. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your readiness to hit the ground running.
✨Tip Number 2
Since collaboration is key in this role, practice explaining complex technical concepts in simple terms. This will help you communicate effectively with both technical and non-technical stakeholders during the interview process.
✨Tip Number 3
Prepare for the technical interviews by reviewing common machine learning algorithms and their applications. Be ready to discuss how you've implemented these in past projects, especially focusing on forecasting or risk-based modeling.
✨Tip Number 4
Research the company’s tech stack and familiarize yourself with GCP. Understanding how to build and maintain cloud-based ML services will give you an edge and show that you're proactive about aligning with their infrastructure.
We think you need these skills to ace Senior Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your hands-on experience in ML engineering, particularly focusing on your expertise in Python and any relevant machine learning frameworks like PyTorch or TensorFlow. Include specific projects where you've developed and deployed machine learning models.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention your experience with cloud-based machine learning services, especially if you have worked with GCP. Highlight your ability to collaborate with cross-functional teams and communicate technical concepts effectively.
Showcase Relevant Projects: If applicable, include links to your GitHub or portfolio showcasing projects that demonstrate your skills in developing scalable ML systems. Focus on projects that involve forecasting or risk-based modeling, as these are critical areas for this role.
Prepare for Interviews: Research common interview questions related to machine learning concepts, optimization, and algorithm development. Be ready to discuss your previous work experiences and how they relate to the responsibilities of the role. Practice explaining technical concepts in a way that non-technical stakeholders can understand.
How to prepare for a job interview at Liberis Limited
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with machine learning models, especially in Python. Highlight specific projects where you've developed and deployed models, and be ready to dive into the technical details during the tech interviews.
✨Understand the Business Context
Since this role is within a FinTech organization, familiarize yourself with how machine learning can impact financial decisions. Be ready to discuss how your expertise in forecasting or risk-based modeling can drive data-driven decisions relevant to the company's goals.
✨Prepare for Collaborative Discussions
Collaboration is key in this role. Think of examples where you've worked closely with cross-functional teams. Be ready to explain how you communicated complex technical concepts to non-technical stakeholders, as this will demonstrate your ability to bridge the gap between teams.
✨Demonstrate Problem-Solving Skills
During the interviews, especially the project discussion, be prepared to tackle hypothetical technical challenges related to machine learning pipelines. Show your thought process and how you approach problem-solving independently, as this reflects the autonomy expected in the role.
