Quantitative Researcher

Numeus; Greater London; Hybrid; GBP 93,000 - 188,000; Full time

Job summary

A digital asset investment firm based in Greater London is seeking a Quantitative Researcher to develop systematic trading strategies and sophisticated investment models. You will utilize machine learning and analytical techniques on large datasets while collaborating with a team of experts in trading and technology. A solid educational background in technical fields is required, along with programming skills in Python and C++. The anticipated salary range is between $125,000 and $250,000 with additional benefits.

Benefits

Health and dental benefits

Discretionary bonus

Qualifications

• Educational background in technical field, preferably Mathematics, Statistics, Physics or Theoretical Computer Science.
• Solid experience in model building, backtesting, parameter optimization routines.
• Strong analytical and statistical modeling skills.

Responsibilities

• Develop sophisticated investment models using scientific approaches.
• Apply quantitative techniques to a vast array of datasets.
• Work closely with developers to translate models into production code.

Skills

Model building

Data analysis

Statistical modeling

Python

C++

Education

Mathematics, Statistics, Physics or Theoretical Computer Science

Job description

Numeus is a diversified digital asset investment firm built to the highest institutional standards, combining synergistic businesses across Alpha Strategies, Trading, and Asset Management.

Numeus was founded by successful executives with decades of experience across the finance, blockchain and technology industries, with a shared passion for digital assets. Our values are grounded in an open approach based on connectivity, collaboration, and partnerships across the digital asset ecosystem. People and technology are at the core of everything we do.

We are looking for a Quantitative Researcher who can help us develop alpha through systematic trading strategies. You will work closely with experienced researchers, traders, and a technology team with deep domain expertise. The digital asset space is young, fast-evolving, and filled with innovative sources of alpha and trading opportunities.

Key Responsibilities

• Employ a rigorous scientific approach to develop sophisticated investment models and deliver insights into how markets behave
• Apply quantitative techniques, like machine learning, to a vast and innovative array of datasets
• Create and test complex investment ideas and develop algorithms that lead to trading decisions
• Analyze investment model performance and behavior, aiming for constant improvements
• Work closely with our team of experienced developers to translate investment models into production code

Skill Set and Qualifications

• Educational background in technical field, preferably Mathematics, Statistics, Physics or Theoretical Computer Science
• Solid experience in model building, backtesting, parameter optimization routines, execution engine design and performance tracking
• Strong analytical skills; experience working with, and analyzing, large datasets
• Strong mathematical and statistical modeling skills (e.g. time-series)
• Ability to think independently, creatively approach data analysis, and communicate complex ideas clearly
• Basic knowledge of the digital asset space is beneficial, but not required
• Intermediate programming background in Python and C++
• Ability to travel periodically between our offices in NYC, London and Zug, Switzerland

Are you keen to work in a well-resourced startup environment, where your ideas, experience, and drive to find creative solutions makes a difference? We’d like to hear from you.

The salary for this role is anticipated to be between $125,000 and $250,000. This anticipated salary range is based on information as of the time this post was created. This role may also be eligible for additional forms of compensation and benefits, such as a discretionary bonus, health, dental and other benefits plans. Actual compensation will be carefully determined based on a number of candidate factors, including their skills, qualifications and experience.