We are building a world-class systematic data platform which will power the next generation of our systematic portfolio engines.

The latency critical trading group is looking for a Data Scientist/Engineer to join our growing team. The team consists of low latency linux engineers, network engineers, datacenter engineers, and C++ engineers who are responsible for building our low latency stack.

This is an opportunity for individuals who are passionate about quantitative investing. The role builds individual’s knowledge and skills in four key areas of quantitative investing, namely: data, statistics, technology, and financial markets.

Desirable Candidates

• Ph.D. or master's in computer science, mathematics, statistics, or other field requiring quantitative analysis or equivalent experience

• Three years of financial industry experience

• Experience working with systematic investing teams is a plus

• Programming expertise in Python and C++

• Data analysis experience with R, MatLab, scitoolkit, pytorch, or similar

• Programming skills in SQL

• Strong problem-solving skills

• Effective communication skills

• Strong understanding of modern statistical testing methods

Job Responsibilities

• Monitoring and Analysis

  • Accessing quality of all captured market data - both live and historical

  • Inventorying all gaps and handling closing gaps via multiple techniques (other internal captures, vendor data, exchange data, etc)

  • Extending parsing and identification of other gaps 

  • Understanding and Documenting session times including holiday schedules

  • Comparing day-on-day changes in latency, data rate, bursts, etc

• Research

  • Documenting history of exchange microstructure behavior changes

  • Understanding and Documenting message timestamp rules for each exchange

  • Documenting history of session schedule changes and protocols

  • Alpha research and presentation to portfolio managers and trading teams

  • Building technology tools to acquire and tag datasets

  • Engaging with vendors, brokers to understand characteristics of datasets

  • Interacting with portfolio managers and quantitative analysts to understand their use cases

  • Building exchange microstructure expertise and helping educate clients as needed

  • Analyzing datasets to generate key descriptive statistics

  • Cleaning historical data

  • Utilizing and maintaining world-class data processing and transformation techniques

• Tools

  • Building data analysis tools on top of our captured data

  • Improving data visualization tools and capabilities

• Technical

  • Consolidating several PCAPs in a single PCAPNG with meta-data to represent capture location/time across capture regions

  • Assessing PTP (Precision Time Protocol) quality within a sole source and across multiple Manage large data sets in a hybrid multi-cloud/on-prem sources environment

  • Build performant systems able to analyze terabytes of data quickly