Machine Learning Scientist

Strong Analytics is seeking Machine Learning Scientists to help design and develop machine-learning based solutions for clients ranging from startups to Fortune 500s. ML Scientists are core contributors at Strong, with responsibilities such as building data analysis pipelines, designing/training/validating machine learning models and algorithms, and collaborating with colleagues and clients on state-of-the-art research. The ideal candidate is proficient in Python and/or R, SQL, and has prior experience working on ML solutions from concept to production.

At Strong, your work will have an immediate impact on clients addressing real world challenges. Our work spans industries such as manufacturing, gaming, agriculture, automotive, and healthcare, among others. We pride ourselves not only in building the right solutions for our clients through research and development, but in implementing and scaling up those solutions through strong engineering. This role thus requires a deep expertise in applying data science and machine learning to real-world problems where data must be gathered, transformed, cleaned, and integrated into a larger architecture.

Responsibilities

• Work with other machine learning scientists and engineers to develop custom ML solutions for a variety of industries and use cases

• Develop, train, and validate machine learning algorithms

• Apply techniques such as classification, clustering, regression, NLP, deep learning (CNN, RNN, GANs), time series forecasting, and Bayesian methods to build scalable solutions

• Help create generalized solutions out of specific use cases

• Help identify areas of opportunity and improvement within projects

• Research new techniques and technologies in the space of ML and AI, and evaluate their potential for business use cases

• Collaborate in a fully remote, Agile-like environment using tools like Slack and Git (with a lot of uninterrupted development time)

Applicants must reside in either the US or Canada. All applicants will be considered based on their experience and demonstrated skill/aptitude, not formal education. Candidates will be evaluated based on their experience in the following areas:

• Statistical modeling and hypothesis testing

• Designing, training, and validating results from a breadth of machine learning algorithms

• Writing clean, efficient SQL

• Building and testing Python applications

• Building deep neural networks with modern tools, such as PyTorch or Tensorflow

• Collaborating via Git

Applicants should have the ability to travel infrequently (<5% of your time) for team meetings, conferences, and occasional client site visits.