Qureos

Find The RightJob.

We’re looking for a hands-on Senior Data Scientist / ML Engineer to help build and deploy scalable machine learning solutions across financial products.

This is a practical, business-focused role for someone who enjoys taking models from experimentation through to production and working closely with engineering, product, and business teams to drive measurable impact.


What You’ll Do:

  • Build and deploy machine learning models for real-world business problems
  • Develop end-to-end ML pipelines including data preparation, training, validation, deployment, and monitoring
  • Work with large and complex datasets to generate actionable insights
  • Partner with engineering teams to productionize models and APIs
  • Collaborate with stakeholders across product, risk, operations, and analytics
  • Improve model performance, reliability, and scalability
  • Contribute to MLOps, automation, and deployment best practices
  • Support AI and advanced analytics initiatives across the business


Required Skills & Experience:

  • 6+ years of experience in Data Science, Machine Learning, or ML Engineering
  • Strong Python and SQL skills
  • Experience building and deploying ML models into production environments
  • Strong understanding of machine learning fundamentals and model evaluation
  • Experience working with cloud platforms (AWS, Azure, or GCP)
  • Experience with tools/frameworks such as:
  • scikit-learn
  • PyTorch or TensorFlow
  • Spark or Databricks
  • Docker / CI-CD pipelines
  • Experience building APIs or production services
  • Ability to work with both technical and non-technical stakeholders


Preferred:

  • Banking, fintech, or financial services experience
  • Experience with customer analytics, fraud, or decisioning models
  • Familiarity with MLOps and model monitoring
  • Exposure to NLP, LLMs, or Generative AI use cases
  • Experience with Airflow, MLflow, Kubernetes, or Hadoop ecosystems


We value people who:

  • Take ownership and solve problems end-to-end
  • Can balance experimentation with production delivery
  • Think commercially and focus on business outcomes
  • Communicate clearly and work well across teams
  • Enjoy building scalable, reliable systems

Tech Environment

Python • SQL • Databricks • Spark • Docker • APIs • Cloud Platforms • MLflow • CI/CD • Power BI

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