Mid Level Chicago Startup and Tech Jobs

4

Machine Learning Engineer

Stats Perform
Chicago
6 days ago
+24
We are looking for a Machine Learning Engineer to build world-class machine learning platform solutions. You will be responsible for empowering data scientists and AI scientists by developing a collection of industry-strength platform services to greatly improve scientists productivity and facilitate innovation. Your responsibilities include, but not limited to:

Principal Data Scientist

Strong Analytics
Chicago
3 weeks ago
Strong Analytics is seeking a Principal Data Scientist to join our growing team! Principal Data Scientists take responsibility for designing, executing, and delivering successful projects for our clients. They play a "player coach" role. In this role, you will: Work with clients to scope project requirements and architecture Orchestrating the project team (data scientists, engineers, and dev-ops) to successfully build out the project roadmap

Senior Data Engineer

Ascent
Chicago
5 months ago
+28
Create a microservice ecosystem behind a consistent, well-documented API. Design and implement data models and database layers that support our machine learning and business intelligence activities. Use creativity and independent thinking to solve technical problems. Communicate clearly and effectively with technical and non-technical colleagues about our data engineering projects. Work closely with data scientists to understand their needs and processes. Work closely with our whole technology team to successfully maintain our data platform alongside the broader technology stack. Implement with an emphasis on tests, maintainability, and clean coding practices to produce simple solutions and reduce technical debt.

Data Scientist

Strong Analytics
Chicago
5 months ago
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. Creating and interacting with RESTful APIs. Managing *nix servers. Writing unit tests. Collaborating via Git.

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