Data Scientist

Role overview

As a Data Scientist, you sit at the earliest and one of the most critical stages of our research process. Your day-to-day work spans data across different markets, frequencies, and levels of granularity, including market data, fundamentals, reference data, and alternative datasets. Collecting, curating, and analyzing these large-scale datasets is a major source of insight that drives ongoing improvements to JQ's trading strategies. You will build automated tooling, explore and interrogate data, work closely with our research and trading teams, and coordinate with external partners (such as data vendors, brokers, and exchanges) to keep our day-to-day trading data workflows stable and reliable.

What we look for

We want candidates with strong foundations in statistics, sharp quantitative intuition, exceptional attention to detail and rigor, a collaborative mindset and strong problem-solving skills, clear communication, and a high sense of ownership and responsibility.

Responsibilities

• Data analysis

Parse, analyze, and deeply understand datasets; perform reconciliation, validation, and quality checks; identify and refine workflows in the data request pipeline to increase data value; support researchers with data cleaning and feature engineering.

• Data engineering

Develop tools to classify, onboard, and reconcile data; use a modern Python/C++ data stack to onboard datasets, explore data at scale, and automate recurring tasks.

• Data debugging

Detect data anomalies and trace issues to their root cause; apply sound deductive reasoning and rigorous technical analysis, and coordinate with multiple stakeholders across the data pipeline to resolve incidents.

• Production support

Proactively monitor data pipeline health, handle internal data user requests, and resolve issues within tight turnaround expectations.

Requirements

1. Bachelor's degree or above from a well-regarded university in Statistics, Mathematics, Physics, Computer Science, Electrical Engineering, Automation, or a related field.
2. ~2 years of relevant experience, or new graduates with strong internships in data processing / data-centric roles.
3. Comfortable with Linux command-line workflows; strong understanding of data pipelines and analytical methods; proficient in Python.
4. Genuine passion for data; numerically sensitive with strong intuition; familiar with data cleaning concepts and techniques.
5. Careful, reliable and able to spot issues in the details, and strong at communication and summarizing findings and learnings.