Data Scientist, Post Trade Analytics at Jump Trading International Limited

, Shanghai, China -

Full Time

Start Date

Immediate

Expiry Date

20 Apr, 26

Salary

0.0

Posted On

20 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Data Science, Analytics, Problem-Solving, Statistics, SQL, Machine Learning, Network Protocols

Industry

Financial Services

Description

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Jump Trading's Post Trade Analytics Team plays a crucial role in measuring and optimizing performance and trade execution across all global markets where the firm operates. This team is responsible for developing, operating, and maintaining performance measurement solutions, as well as conducting a wide range of post-trade analyses for various departments, including trading, business development, and other central teams. Daily, the team's core infrastructure collects, organizes, and stores billions of data points related to trading performance. As a Data Scientist on the PTA team, you will conduct cutting-edge analyses on this extensive data to uncover competitive edges, answer complex business questions, and identify root causes of underperformance. This role offers a comprehensive view into Jump's business through the lens of data, along with the opportunity to develop both trading and technical expertise. Your work will significantly contribute to Jump's success by mining and enriching data to drive informed decisions and strategies. The ideal candidate will have the ability to work closely with and collaborate across trading and trading infrastructure teams. We are looking for an individual who is instinctively curious, self-driven, keen to dive into the complex trading world, and has demonstrated an ability to independently drive tasks and projects to completion in a team environment. What You'll Do: Partner with trading teams to perform post trade analysis on trading performance, linking PnL metrics with latency and other infrastructure measurements Identify relationships between business metrics and changes in the infrastructure technology Conduct research for the purpose of modeling exchange performance Develop mathematical methodology to quantify the impact that specific internal applications have on general performance Design innovative alert detection algorithms to proactively detect new business opportunities, performance degradation or abnormal PnL-impacting events Participate in the building of new and innovative datasets Develop and improve quantitative research frameworks Other duties as needed Skills You’ll Need: At least 5+ years of programming experience in Python with a data science and analytics focus Strong data intuition combined with a healthy skepticism for easy conclusions Excellent problem-solving abilities Experience in documenting and communicating analysis to both technical and business audiences, including members of the senior leadership team Background in statistics Experience with SQL is a plus. Practical experience with machine learning technics is a plus. Understanding of network protocols (IP, UDP, TCP, Ethernet) is a plus Reliable and predictable availability Shanghai based

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Responsibilities

As a Data Scientist on the Post Trade Analytics team, you will conduct analyses on extensive trading data to uncover competitive edges and answer complex business questions. You will also develop methodologies to quantify the impact of internal applications on performance and design alert detection algorithms.