Quantitative Developer - Python & PyData Research Tools

Arrowstreet Capital, Limited Partnership

Boston (MA)

Vor Ort

USD 155.000 - 260.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

Schaffe es an den ATS-Filtern vorbei

Zusammenfassung

Arrowstreet Capital, a Boston-based systematic investment firm, seeks Quantitative Developers to join the Research group. You will develop Python/R code, build data science tooling, and run analyses on large datasets to support trading decisions.

The role emphasizes production-grade software, collaboration with software engineers, and exploration of new PyData libraries to improve investment research pipelines.

Qualifikationen

  • Undergraduate or graduate degree with quantitative focus (CS/math/finance).
  • Strong analytical, quantitative, problem-solving skills; excellent communication and data viz capabilities.
  • Production-grade Python experience for data analytics in finance or investments; PyData ecosystem familiarity.

Aufgaben

  • Write and maintain Python and R code supporting investment research workflows.
  • Design software to enhance data science tech stack and data feeds from vendors.
  • Perform exploratory statistical analysis on large structured and unstructured datasets.
  • Improve performance in data analysis and numerical programming code.
  • Run POCs to evaluate new PyData technologies and libraries.
  • Stay updated on PyData ecosystem and evaluate new tools.

Kenntnisse

Python
pandas
numpy
time-series
data visualization

Ausbildung

CS/Math/Finance degree

Tools

Python
pandas
numpy
Hadoop
Spark
Kafka
SQL
Unix/Linux
DevOps

Jobbeschreibung

Arrowstreet Capital, a Boston-based systematic investment firm, seeks Quantitative Developers to join the Research group. You will develop Python/R code, build data science tooling, and run analyses on large datasets to support trading decisions.

The role emphasizes production-grade software, collaboration with software engineers, and exploration of new PyData libraries to improve investment research pipelines.

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