The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.
Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions – LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks – to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.
Knowledge and Experience
3+ years of experience as a Data Scientist, ML Engineer, or in a similar analytical role.
Strong programming skills in Python, with experience writing reusable libraries and working with data manipulation and ML libraries (e.g., pandas, NumPy, scikit-learn, PyTorch/TensorFlow).
Solid grounding in statistics and machine learning: feature engineering, model selection, validation, and interpreting results for a business audience.
Hands-on experience with LLMs and Agentic AI: prompt engineering, retrieval-augmented generation (RAG), tool/function calling, and building or consuming agent frameworks.
Advanced proficiency in SQL and experience working with large-scale databases (e.g., PostgreSQL, MSSQL, Oracle).
Experience with AI/ML workflows, supporting model training, inference, and evaluation pipelines in production environments.
Genuine curiosity and a strong appetite to learn – eager to bring existing knowledge to the team and to grow it further.
Preferred Knowledge and Experience
Background in finance, trading systems, or financial market data.
Experience building or consuming MCP (Model Context Protocol) servers and clients.
Experience with workflow automation / orchestration platforms such as n8n, Airflow, or similar.
Experience with data visualisation and BI tooling for communicating analytical results.
Exposure to real-time data processing technologies (e.g., Kafka, Spark Streaming).
