We’re working with a leading investment banking consultancy expanding its onshore AI & Data Engineering capability. They’re looking for a hands-on Data Scientist / Quantitative Engineer with strong fixed income domain knowledge, Databricks engineering, and financial modelling experience to support a front-office trading analytics programme.
Key Responsibilities
Partner with front-office traders to gather requirements and validate model results.
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Build, optimise, and productionise financial and ML models – e.g. Monte Carlo simulations, stochastic processes, and time-series forecasting.
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Design and implement data pipelines and model training workflows in Databricks (Spark, Delta, MLflow).
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Lead and mentor offshore data scientists and data engineers, setting technical direction and reviewing deliverables.
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Collaborate with BI and DevOps teams to ensure scalable, secure, and automated ML delivery.
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Apply deep learning techniques (RNN/LSTM/CNN) on Spark clusters for large-scale financial data.
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Contribute to LLM and RAG (retrieval-augmented generation) initiatives where applicable.
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Communicate insights and recommendations clearly to trading, technology, and business stakeholders.
Required Skills & Experience
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3–5+ years of experience in data science, ML engineering, or quantitative analytics.
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Strong background in fixed income products (bonds, spreads, coupons, yield curves).
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Proven experience implementing financial models (Monte Carlo, Markov, stochastic processes).
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Proficient in Python, PySpark, and SQL for modelling and data wrangling.
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Hands-on with Databricks and distributed computing frameworks (Spark, Dask).
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Solid understanding of cloud platforms – Azure (preferred), AWS, or GCP.
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Strong mathematical foundations – probability, statistics, linear algebra, optimisation.
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Experience delivering ML models at scale, ideally with MLflow, TensorFlow, or PyTorch.
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Excellent communication and stakeholder engagement – able to hold your own with traders
