Head - Data Science - Bigcommerce - San Francisco, Sydney

Job description

Head of Data Science
Location Sydney, Australia or San Francisco, CA

We start by identifying high-impact problems and asking the right questions to develop viable solutions with a focus on the product, business and emerging opportunities at Bigcommerce. We currently collect the necessary data (lots of it!) to answer these questions and need to apply known analysis and modeling techniques (and sometimes devise new ones) to this data to get answers. We often work with sales, marketing, finance, people, monetization and product teams to refine our approach. Once we get repeatable answers, we not only produce reports and presentations but also develop software frameworks and tools so that the next time we have a similar question, we don't have to work as hard to get an answer. Yes, we are lazy that way ;-).

The role asks for the usual suspects in terms of quantitative know-how: Good understanding of fundamentals of statistics and expertise with various machine learning techniques are must-haves. As for the technology stack, deep knowledge of (or desire to swiftly learn) Python in the context of scientific coding and distributed computing and some SQL are pretty much required to get anything done. As you can tell, we expect Quantitative Engineers to be highly data-driven and quite comfortable working both as a quantitative scientist and a generalist software engineer.

Responsibilities

Work closely with sales, marketing, finance, people, monetization and product teams to provide impactful analysis and insights.
Drive the collection of new data and the refinement of existing data sources.
Apply known statistical and machine learning techniques and devise new ones to understand and analyze data.
Implement quantitative methodologies to run on distributed systems in high-quality code using software engineering best practices.
Communicate findings via reports and presentations to internal and external audiences of all levels.

Desired Skills and Experience

Requirements

MS/PhD in computer science, computational statistics, computational econometrics, operations research or related field.
Hands-on, deep knowledge of Python as a user of scientific libraries (numpy, scipy, pandas, scikit-learn, etc.) and as a generalist. Alternatively, R or MATLAB with strong C++ or Java experience.
2+ years experience and an excellent understanding of machine learning techniques (classification, clustering, dimensionality reduction, etc.).
2+ years hands on experience working with large datasets (>10TB) on distributed systems.
Good understanding of fundamentals of statistics.
Good understanding of fundamentals of SQL.

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