Senior Quantitative Analyst

Building robust, object-oriented modelling frameworks, optimising code for speed and memory, and integrating various financial libraries. You'll be writing production-quality code, not just scripts.

Designing schemas for new datasets, writing complex stored procedures, and using advanced window functions and CTEs for intricate time-series analysis. You'll also be optimising query performance for large datasets.

Using terminal APIs (like Bloomberg's BQL/BDP) to programmatically pull and integrate vast, real-time and historical datasets into your research pipelines. You'll be automating data acquisition.

Managing complex branching strategies (e.g., git-flow), resolving tricky merge conflicts, and conducting thorough code reviews via pull requests. You'll be enforcing repository standards and ensuring code integrity.

Provisioning and managing cloud resources (like AWS EC2 for compute, S3 for data storage) to run large-scale backtests, simulations, and model training. You'll be building scalable solutions in the cloud.

Building complex, interactive dashboards that allow traders and portfolio managers to intuitively explore model outputs and performance metrics. You'll also be automating reporting pipelines and using VBA for complex Excel tasks if needed.

• Lead Quantitative Analysts (your peers and direct manager)
• Portfolio Managers (who use your models to make decisions)
• Trading Desk Heads (who execute based on your signals)
• Risk Management Team (who validate your models)
• Technology & Engineering Teams (who help deploy your solutions)

• Financial Data Vendors (e.g., Bloomberg, Refinitiv)
• Academic Researchers (for collaboration or staying current)
• External Auditors (occasionally, for model validation reviews)

Important within 12-18 months. Simple ML models are becoming commoditised. The edge will come from understanding and applying more sophisticated techniques like Reinforcement Learning for optimal execution, Causal Inference for understanding market drivers, or Graph Neural Networks for relationship modelling in complex financial networks.

Reinforcement Learning in Trading · Causal Inference Techniques · Graph Neural Networks (GNNs) · Explainable AI (XAI) in Finance · Adversarial Machine Learning

• This quarter: Take an online course or read a textbook on Reinforcement Learning or Causal Inference.
• Next quarter: Identify a small internal problem where you could apply one of these advanced techniques as a proof-of-concept.
• Month 6: Present your findings and the potential value of these techniques to the broader quant team.
• Month 9: Begin integrating XAI techniques into your existing models to improve interpretability.

Quick win: Start by exploring libraries like `econml` for causal inference in Python, or `Stable-Baselines3` for RL. Even playing with these on toy datasets will build foundational understanding.

Important within 12 months. As datasets grow and backtests become more complex, running everything on a single machine won't cut it. You'll need to understand how to scale your analysis across clusters of machines, making your models faster and capable of handling more data.

Apache Spark & Dask · Containerisation (Docker, Kubernetes) · Cloud-Native Data Warehousing · Orchestration Tools (Airflow, Prefect)

• This quarter: Complete a tutorial on Apache Spark or Dask, running a simple data processing job on a small cluster.
• Next quarter: Containerise one of your existing Python models using Docker and deploy it locally.
• Month 6: Work with the tech team to understand our current cloud architecture and identify opportunities to use distributed computing for a specific project.
• Month 9: Propose and implement a small-scale distributed backtesting framework for a new strategy.

Quick win: Experiment with Docker by containerising a simple Python script. It's a fundamental step that will make your code much more portable and easier to deploy at scale.