Senior Data Scientist

intro; London; Hybrid; GBP 60,000 - 80,000; Full time

Job summary

A leading FinTech company is seeking a Senior Data Scientist for their London office. You will work on developing ML models and graphical analyses to optimise payment workflows and enhance operational efficiency. This role offers an exciting opportunity to drive innovation in data science and payments with a focus on impactful projects.

Qualifications

• Experience in data science and graph analytics, preferably in financial or payments industry.
• Strong understanding of graph databases and algorithms.
• Proficiency in Python or R for large-scale data processing.

Responsibilities

• Develop ML models to optimise payment workflows and detect anomalies.
• Apply graph analytics to improve fraud detection mechanisms.
• Build and maintain scalable data pipelines for payment data.

Skills

Graph analytics

Data science

Machine learning

Statistical analysis

Communication

Collaboration

Education

Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Mathematics, Physics, or related field

Tools

Spark

AWS

GCP

MLflow

SageMaker

Job description

Direct message the job poster from intro

Headhunting made human | Simplifying success for teams and careers, with intro

Senior Data Scientist - Hybrid (central London)

A FinTech specialising in cloud based payment solutions is looking for an experienced Senior Data Scientist to join their growing team in London. This innovative organisation is transforming the payments landscape by delivering seamless, data driven solutions for businesses worldwide.

This is an opportunity to work on impactful projects, leveraging advanced data science techniques to optimise payment processing, detect anomalies, and improve operational efficiency. If you’re passionate about applying your skills in data science to drive innovation in payments, this role is for you.

What You’ll Be Doing

• Developing ML models to optimise payment workflows, detect anomalies, and predict payment success rates.
• Applying graph analytics techniques to uncover hidden relationships in transaction data and improve fraud detection mechanisms.
• Building and maintaining scalable data pipelines to support real time and batch processing of payment data.
• Leveraging Spark for distributed data processing and ML on large datasets.
• Collaborating with cross functional teams, including product, engineering, and operations, to translate business needs into data-driven solutions.
• Conducting deep statistical analysis and modelling to generate actionable insights for improving the payment platform.
• Mentoring junior team members and championing best practices in data science, including explainable AI and ethical model design.

What They’re Looking For

• Education: Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Mathematics, Physics, or a related field.
• Experience: Professional experience in data science and graph analytics, preferably in the financial or payments industry.
• Expertise: Strong understanding of graph databases, graph algorithms, and graph analytics techniques.
• Technical Skills: Proficiency in programming languages such as Python or R, with experience using Spark for large-scale data processing and machine learning.
• Communication: Excellent communication skills with the ability to explain complex models to technical and non technical stakeholders alike.
• Collaboration: Proven ability to work collaboratively in both onsite and remote environments, contributing effectively to a distributed team.
• Experience with cloud platforms such as AWS or GCP.
• Familiarity with transaction data processing and anomaly detection techniques.
• Knowledge of deploying models into production using MLOps tools like MLflow or SageMaker.

intro Consulting Ltd are proud to represent this forward thinking FinTech client. We act as their trusted recruitment partner, connecting top talent with meaningful opportunities.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

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