Quantitative Researcher, RBC Capital Markets, LLC, New York, NY:

Royal Bank of Canada•New York, NY

3d•$185,000

About The Position

Quantitative Researcher, RBC Capital Markets, LLC, New York, NY: Conduct quantitative research on algorithmic trading, routing mechanisms, and trading costs. Analyze global markets and alternative data sources using kDB+/Q and Python. Build ETL (Extract, Transform, Load) pipelines for out-of-core / big datasets using Python. Leverage expertise in time series, signals processing, and statistical inference to estimate various characteristics of market data (e.g. global equity trading volume). Provide key insights and support for the sales and trading team by instrumenting metrics and implementing monitoring / alerting tooling. Leverage expertise in deep learning frameworks such as PyTorch to integrate state of the art algorithms and artificial intelligence tools with trading algorithms. #LI-DNI Full time employment, Monday – Friday, 40 hours per week, $185,000 per year. The base salary for this job is $185,000 per year. This salary does not include other elements of total compensation, including a discretionary bonus and benefits such as a 401(k) program with company-matching contributions; health, dental, vision, life and disability insurance; and paid time-off plan. RBC’s compensation philosophy and principles recognize the importance of a highly qualified global workforce and plays a critical role in attracting, engaging and retaining talent that: · Drives RBC’s high performance culture · Enables collective achievement of our strategic goals · Generates sustainable shareholder returns and above market shareholder value To Apply: Please click “Apply Now” Button

Requirements

Master’s degree in Computer Science, Financial Engineering, Mathematics, Physics, or a related field and 3 years of work experience.
Must have 3 years of experience in: Writing software in Python to design, maintain and operate batch and stream processes for model fitting and inference pipelines (including metrics, monitoring, and alerting).
kDB+ / Q for conducting statistical analysis on large datasets.
Equity algorithmic trading to produce models that will be consumed by algorithmic trading software.
Quantitative research, modelling (time series, linear models).
Applied machine learning, and data visualization.
Global equity markets and microstructure.

Responsibilities

Conduct quantitative research on algorithmic trading, routing mechanisms, and trading costs.
Analyze global markets and alternative data sources using kDB+/Q and Python.
Build ETL (Extract, Transform, Load) pipelines for out-of-core / big datasets using Python.
Leverage expertise in time series, signals processing, and statistical inference to estimate various characteristics of market data (e.g. global equity trading volume).
Provide key insights and support for the sales and trading team by instrumenting metrics and implementing monitoring / alerting tooling.
Leverage expertise in deep learning frameworks such as PyTorch to integrate state of the art algorithms and artificial intelligence tools with trading algorithms.