Data Scientist...

Job Description

We are looking for a Data Scientist to help the Applied AI team in the Bank, holding expertise in machine learning and AI capabilities, working on ML/AI model development, evaluation and deployment based on large-scale data processing.

Long-term project

London

Hybrid - Max Three Days in the office

Rate 550-600

Position Overview:

Conducts strategic data analysis, identifies insights and implications and makes strategic recommendations, develops data displays that clearly communicate complex analysis.

Collaborate with stakeholders across cross-functional teams to understand data needs, translate them into impactful data-driven solutions and deliver these in partnership with the technology team.

Develop and integrate functionality to ensure adherence with best-practices in terms of data management and data governance.

Mine and analyse data from various banking platforms to drive optimisation and improve data quality.

Collaborate on design and implementation of workflow solutions that provide long-term scalability, reliability, and performance, and integration with reporting.

Required Skills:

• Expertise and hands-on experience in advanced programming using: SAS / Python / pySpark and SQL for data mining; additional experience and knowledge of Big Data tools preferred.
  
• Excellent Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc).
  
• Understanding of Gen AI models, Vector databases, Agents, and follow the market trends. Its desirable to have a hands-on experience on these.
  
• Substantial experience using tools for statistical modelling of large data sets
  
• Some familiarity with data workflow management tools such as Airflow as well as big data technologies such as Apache Spark or other caching and analytics technologies
  
• Expertise in model training, Statistics, model evaluation, deployment and optimisation, including RAG-based architectures.