Data Scientist

Primis
London, UK
3 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

Amazon Web Services Microsoft Azure Continuous Integration Information Engineering DevOps Monitoring of Systems Apache Hive Python (Programming Language) Machine Learning Rapid Prototyping Process Apache Spark Machine Learning Operations

Job description

We’re partnered with a boutique financial consulting firm seeking experienced Data Scientists to join their growing data practice in London. Known for their collaborative culture and high-impact work with leading financial institutions, this is a rare opportunity to take ownership of complex data science challenges while shaping the direction of a high-performing team.

You’ll lead end-to-end delivery of machine learning solutions - from early proof-of-concept through to production - working alongside engineers, domain experts, and business stakeholders across the financial services sector.

What you’ll be doing:

  • Taking ownership of data science projects from initial concept and rapid prototyping through to live production deployment
  • Building and iterating on machine learning models that address real-world business problems across financial services
  • Partnering with engineering, business, and domain teams to bridge the gap between commercial goals and technical solutions
  • Serving as the go-to expert on ML system design, model tuning, and bringing solutions to production at scale

Requirements

  • Proven ability to build and ship production-ready data science solutions using Python and the wider ML ecosystem
  • Deep practical knowledge of applied machine learning, spanning model development through to data engineering
  • Comfortable working across major cloud platforms (Azure, AWS or GCP) with hands-on exposure to tools like Spark, Hive or Redshift
  • A track record of leading cross-functional teams and communicating technical concepts to non-technical audiences
  • Experience with MLOps practices - CI/CD, model monitoring, DevOps integration

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