Strong Analytics is seeking a data scientist to join our team in developing machine learning solutions, building  statistical models, and generally helping our clients discover value in their data.

At Strong, 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 statistics and machine learning to real-world problems where data must be gathered, transformed, cleaned, and integrated into a larger architecture.

We offer a comprehensive compensation package, including:

• Competitive salary
• Profit sharing or equity, based on experience
• 100%-covered Health insurance for employees, 75%-covered dependents
• Four weeks PTO
• Work-from-Home Wednesdays
• 401(k) matching
• Personalized monthly perks with matched charitable donations

Candidates will be evaluated based on their experience in the following areas (though no one is expected to be an expert in all of these):

• Statistical modeling and hypothesis testing
• Designing, training, and validating results from a breadth of machine learning algorithms
• Writing clean, efficient SQL
• Integrating with various RDBMS (e.g., Postgres, MySQL) and distributed data stores (e.g., Hadoop)
• Building Python applications
• Deploying applications into cloud-based infrastructures (e.g., AWS)
• Building deep neural networks with modern tools, such as PyTorch or Tensorflow
• Building, testing, and deploying computer vision based solutions
• Building, testing, and deploying reinforcement learning based solutions
• Creating and interacting with RESTful APIs
• Managing *nix servers
• Writing unit tests
• Collaborating via Git

Applicants with a PhD in a quantitative field are preferred; however, all applicants will be considered based on their experience and demonstrated skill/aptitude.

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