Job Description

Every business on the face of Earth must, in some way, do bookkeeping, accounting, and financial planning to operate. At the outset, these functions may seem like mundane facts-of-life in the process of running a business; however, the skill with which a company does them can have a profound impact not only on their business, but also the world. A poorly forecasted budget, could mean the abrupt end to the clinical trial of a potentially life-saving drug. On the other hand, a highly accurate hiring plan can lead to successful team growth that allows a company to design a brand-new material that helps reverse climate change. 

Today, unfortunately, financial management and services are universally manual, tedious, and error prone. At the same time, these processes often follow well-defined rules, abide by industry standardization, and have become increasingly data-rich. Our team, within the Medium Segment Native Cloud Solutions at Sage, builds cloud-based AI-powered features and products that fundamentally change the way businesses operate. 

We are looking for a Principal Machine Learning Scientist in San Francisco to help us ship AI-powered products and services. 

Responsibilities:

  • Building, experimenting, training, tuning, and shipping machine learning models in the areas of: classification, clustering, time-series modeling and forecasting. 
  • Writing production-quality/ optimized code. 
  • Working with product managers and engineers to translate product/business problems into tractable machine learning problems and drive the ideas into production using machine leaning 
  • Working with machine learning infrastructure engineers to ship models. 
  • Presenting findings, results, and performance metrics to stakeholders. 

Minimum Qualifications:

  • MS in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative field. 
  • Strong theoretical and mathematical foundations in linear algebra, probability theory, multivariate optimization. 
  • Have a strong intuition into different modeling techniques and their suitability to different problems. 
  • Strong production level programming skills in Python or C++. 
  • 5+ years of hands-on experience in working with several of: pytorch, tensorflow, numpy, scipy, scikit-learn, pandas. 
  • 5+ years industry experience training and shipping production machine learning models. 
  • Experience communicating projects to both technical and non-technical audiences. 

Preferred Qualifications :

  • PhD in Computer Science, Electrical Engineering, Statistics, Physics, or similar quantitative fields. 
  • Publications in top conferences (ICML, NeurIPS, ICLR, ACL, EMNLP, ICCV). 
  • Experience wrangling data, writing complex SQL queries and basic bash scripting. 
  • You have deep experience with: logistic regression, gradient descent, regularization, cross-validation, overfitting, bias, variance, convex optimization, eigenvectors, sampling, 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 can consume research ideas and papers and translate them into production models. 
  • You don't only use machine learning models but can implement many machine learning and statistical learning models from scratch and know when/how to apply them to real world noisy data. 
  • You’re a deeply curious person and eager to learn and grow. 
  • 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. 

Share this job