Quantitative Research Analyst

Anson McCade
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist/Modeller

Applied Research - Artificial Intelligence - London - VP

Applied Research - Artificial Intelligence - London - Associate

Applied Research - Artificial Intelligence - London - VP

As a Quant Research Analyst of one of their world class quant trading teams, you'd responsible for developing and implementing complex models and algorithms that inform on investment strategies, risk management, and financial decision-making. This role requires a blend of statistical analysis, algorithm development, and deep understanding of financial markets.

Develop and implement models and strategies focused on alpha generation across various asset classes. Use statistical and machine learning techniques to identify market inefficiencies. Perform complex data analysis to uncover patterns and predictive signals in market data. Create robust financial models for forecasting and risk assessment. Quantitative Research: Conduct research to understand market dynamics and investor behavior. Apply quantitative methods to develop strategies that capitalize on market anomalies and trends. Design algorithms for efficient trade execution and portfolio optimization, ensuring they align with alpha-generation goals. Work closely with portfolio managers and traders, providing them with actionable insights and recommendations for alpha-generating strategies. Continuously monitor and analyze the performance of deployed strategies. Refine and adjust approaches based on market feedback and performance data. Effectively communicate complex quantitative strategies and findings to stakeholders, including non-technical audiences, to inform decision-making processes.

Degree in a quantitative field such as Mathematics, Statistics, Physics, Computer Science, or Financial Engineering. Solid experience in quantitative analysis with a proven track record in alpha generation. Strong skills in Python, R, MATLAB, or similar tools for complex data analysis and model development. Exceptional skills in statistical analysis and modeling, with a focus on predictive analytics and pattern recognition. Ability to think creatively to identify new opportunities for alpha generation. Excellent verbal and written communication skills for effective collaboration and presentation of findings. Experience with machine learning, AI, and big data analytics in finance is a plus

AMC/AMO/DMO1046690

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.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.

Neurodiversity in AI Careers: Turning Different Thinking into a Superpower

The AI industry moves quickly, breaks rules & rewards people who see the world differently. That makes it a natural home for many neurodivergent people – including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a career in artificial intelligence, you might have been told your brain is “too much”, “too scattered” or “too different” for a technical field. In reality, many of the strengths that come with ADHD, autism & dyslexia map beautifully onto AI work – from spotting patterns in data to creative problem-solving & deep focus. This guide is written for AI job seekers in the UK. We’ll explore: What neurodiversity means in an AI context How ADHD, autism & dyslexia strengths match specific AI roles Practical workplace adjustments you can ask for under UK law How to talk about your neurodivergence during applications & interviews By the end, you’ll have a clearer picture of where you might thrive in AI – & how to set yourself up for success.