Principal Machine Learning Scientist

San Francisco, California, United States

Full Time

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Company Info

Sage Intacct, Inc.

Computer Software

Company Type: Public Company                                            Size: 501-1,000 employees

Today, nearly every business in the world has to do bookkeeping and accounting in order to file taxes, understand financial health, and most importantly, make strategic business decisions. The process is universally manual, tedious, and error prone. At the same time, the process follows well-defined rules, abides by industry standardization, and has become increasingly data-rich. It’s a product-focused machine learning team’s dream. Sage Intacct Artificial Intelligence Labs “SAIL” is a nimble team within Sage Intacct building the future of cloud accounting by leveraging artificial intelligence. The SAIL team builds capabilities to help businesses make better decisions through data-powered insights. We are looking for a Principal Machine Learning Scientist in San Francisco to help us ship AI-powered products and services. As a part of our cross-functional team, including data scientists and engineers, you will help steer the direction of the entire company’s Artificial Intelligence / Machine Learning effort and you will also be a key individual contributor.

Responsibilities:

  • Solving problems from ideation to production, using machine learning.
  • Experimenting, training, tuning, and shipping machine learning models.
  • Writing production-quality code.
  • Exploratory data analyses and investigations.
  • Working with product managers to translate product/business problems into tractable machine learning problems.
  • Working with machine learning infrastructure engineers to ship models.
  • Presenting findings, results, and performance metrics to team.

Basic Qualifications:

  • MS in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative field.
  • Strong theoretical foundations in linear algebra, probability theory, optimization.
  • Strong programming skills in Python.
  • 4+ years of hands-on experience in working with numpy, scipy, scikit-learn, pandas.
  • 4+ years industry experience shipping production machine learning models.
  • Experience communicating projects to both technical and non-technical audiences.
  • You know what these things are (in no particular order): logistic regression, gradient descent, regularization, cross-validation, overfitting, bias, variance, convex optimization, eigenvectors, relational databases, SQL, latency, computational complexity, sparse matrices.

Preferred Qualifications:

  • PhD in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative fields.
  • Publications in top conferences.
  • Experience writing complex SQL queries.
  • You have deep experience with these things: logistic regression, gradient descent, regularization, cross-validation, overfitting, bias, variance, convex optimization, eigenvectors, relational databases, SQL, latency, computational complexity, sparse matrices.

You may be a fit for this role if you:

  • You’re comfortable with investigating open-ended problems and coming up with concrete approaches to solve them.
  • You know when to use machine learning and when not to!
  • You’re a deeply curious person.
  • You can wrangle data like a pro alligator wrestler and come out relatively unscathed.
  • You often think about applications of machine learning in your personal life.

What it’s like to work here:

You will have an opportunity to work on a small and growing team based in San Francisco in an environment where engineering is central to what we do. The products we build are breaking new ground, and we have a focus on providing the best environment to allow you to do what you do best – solve problems, collaborate with your team and push first class software. We promote an open diverse environment, encourage contributions to open-source software and invest heavily in our staff. Our team is talented, capable and inclusive. We know that great things can only be done with great teams and look forward to building and working with a great people.

Posted: December 4, 2019

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