Quantitative Data Engineer - EOT QEDD - Experienced Hire
Susquehanna International Group, LLP Dublin, Leinster, Ireland
Financial Services · 1,001-5,000 employees
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About the role
You will collaborate with quantitative researchers and traders to optimize algorithms, analyze complex datasets, and develop modeling features. Additionally, you will be responsible for writing, testing, and deploying Python code to support revenue-generating research projects.
What they look for
Requirements
Candidates must hold a STEM degree and possess at least 4 years of professional Python experience with a focus on algorithm design. You are also required to have 3+ years of hands-on experience with data manipulation libraries and proficiency in a Linux environment.
Full description
Overview
Quantitative Data Engineer
At Susquehanna, the frontier of trading is no longer just speed—it’s also about mastering data. The Quantitative Engineering and Data group in Europe is a rapidly expanding group focused on creating algorithms, processes and datasets that enable revenue generation from data at Susquehanna. We partner with Quantitative Researchers, Traders and other engineering teams in London, Dublin and across the globe. We are looking for world leading talent that has the skills to write great Python code and navigate complex data and models.
Job Summary:
You will work closely with Quantitative Research and Trading to optimise quant code and algorithms, analyse data, and develop modelling features for research. As you develop in the role you will gain an intuition into the needs of a quantitative trading business. This intuition coupled with your skills will enable you to deliver real-world value to the company.
- Member of the Quantitative Engineering and Data group
- Responsible for enabling research and trading activities using a range of data science, analysis, and engineering skills.
- Write, test, and deploy Python code that creates new model features, analyses complex datasets, optimizes algorithms/quant code, and defines data orchestration.
- Partner with researchers on revenue generating projects taking on coding tasks that optimise project delivery.
- Develop expertise in Susquehanna’s systems and business.
What we’re looking for
- Degree in a STEM subject with evidence of independent work either from work experience, Masters, or PhD.
- 4+ years professional experience in Python with practical experience solving programming problems through algorithm design.
- 3+ years hands-on experience working with data manipulation libraries (e.g. Pandas, Polars, PySpark).
- Experience working in a Linux environment.
- A desire to work closely with Researchers and accelerate their research.
- The ability to self-manage, self-motivate, and seek process improvement opportunities.
- Strong interpersonal and communication skills that enable you to collaborate effectively in a quantitative domain.
- Attention to detail and ability to react to changing priorities.
- A general interest in quantitative trading.
About Susquehanna
Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.
Susquehanna is an equal opportunity employer. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruitment process, please contact us.
If you're a recruiting agency and want to partner with us, please reach out to recruiting@sig.com. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.
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