Strong Analytics is seeking a data scientist to join our team in developing machine learning pipelines, building and validating statistical models, and helping our clients discover value in their data.
This role requires a deep expertise in applied statistics, as well as experience designing and developing data applications comprising ETL pipelines, machine learning algorithms, and scalable data stores with distributed computational layers (e.g., Hadoop, Spark, Hive).
Candidates will be evaluated based on their experience in the following areas:
- Statistical modeling and hypothesis testing using Python and/or R.
- Designing, training, and validating a breadth of machine learning algorithms.
- Writing complex SQL queries in various RDBMS (e.g., Postgres, MySQL) and distributed frameworks (e.g., Hive).
- Building and deploying Python and/or Scala applications.
- Deploying applications and interacting with cloud-based infrastructures (e.g., AWS).
- Building and validating deep neural networks with modern tools, such as PyTorch or Tensorflow.
- Interacting with and building RESTful APIs.
- Managing *nix servers.
- Writing unit tests.
- Using continuous integration.
- 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.
What We Offer
- Competitive salary
- Profit sharing or equity, based on experience
- Health insurance
- Generous vacation policy
- Flexible work-from-home policy
Please send a cover letter, CV, and work examples (including blog posts and open source repositories!) to email@example.com.