Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist / Quant Engineer

London
3 weeks ago
Create job alert

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.

*

Build, optimise, and productionise financial and ML models – e.g. Monte Carlo simulations, stochastic processes, and time-series forecasting.

*

Design and implement data pipelines and model training workflows in Databricks (Spark, Delta, MLflow).

*

Lead and mentor offshore data scientists and data engineers, setting technical direction and reviewing deliverables.

*

Collaborate with BI and DevOps teams to ensure scalable, secure, and automated ML delivery.

*

Apply deep learning techniques (RNN/LSTM/CNN) on Spark clusters for large-scale financial data.

*

Contribute to LLM and RAG (retrieval-augmented generation) initiatives where applicable.

*

Communicate insights and recommendations clearly to trading, technology, and business stakeholders.

Required Skills & Experience

*

3–5+ years of experience in data science, ML engineering, or quantitative analytics.

*

Strong background in fixed income products (bonds, spreads, coupons, yield curves).

*

Proven experience implementing financial models (Monte Carlo, Markov, stochastic processes).

*

Proficient in Python, PySpark, and SQL for modelling and data wrangling.

*

Hands-on with Databricks and distributed computing frameworks (Spark, Dask).

*

Solid understanding of cloud platforms – Azure (preferred), AWS, or GCP.

*

Strong mathematical foundations – probability, statistics, linear algebra, optimisation.

*

Experience delivering ML models at scale, ideally with MLflow, TensorFlow, or PyTorch.

*

Excellent communication and stakeholder engagement – able to hold your own with traders

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.