Strong Analytics is seeking a data scientist to join our team in developing machine learning pipelines, 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 some larger architecture.
We offer a comprehensive compensation package, including:
- Competitive salary
- Profit sharing or equity, based on experience
- Health, dental, vision, and life insurance
- Four weeks paid vacation
- 401k with employer matching
Candidates will be evaluated based on their experience in the following areas (though no one is expected to be an expert in each of these):
- Statistical modeling and hypothesis testing
- Applying machine learning to real-world problems
- Writing clean SQL and ETL pipelines
- Building Python applications
- Building deep neural networks with modern tools, such as PyTorch or Tensorflow
- Integrating with various RDBMS (e.g., Postgres, MySQL) and distributed data stores (e.g., Hadoop)
- Deploying applications into cloud-based infrastructures (e.g., AWS)
- 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.