Remote job description

Who we are:

We are industry veterans and data-scientists using innovative technology to fearlessly reinvent the future of freight. As the 'nerds of logistics', we seek intelligence in data to solve deep rooted inefficiencies in the industry. We give shippers, brokers and carriers access to our data connections that link supply and demand and a suite of award-winning solutions to strike the perfect balance of cost and service. We're creating a more efficient and environmentally responsible way to move more with less.

Who you are:

You believe in game-changing innovations and are excited about reimaging a 700 billion dollar industry. You know how to take ideas, optimize them, and push it into production code using Python. You have experience putting your machine learning models in production, and know your way around databases and ETLs. You like working in a hands-on environment and enjoy teamwork.

Where we are:

Loadsmart was founded in New York and is currently headquartered in Chicago, IL. Our teams operate remotely from different parts of the United States as well as in several locations across Latin America.

The role:

We are looking for a Sr. Machine Learning Engineer to work remotely from Lat-Am. You'll join us in obsessing about transformational technology as part of one of our Data Science teams. You should have experience and proven ability to analyze data, create machine learning models, implement APIs to serve these models, and write ETLs and data pipelines to push these models to production. Our technology stack on the backend is primarily on top of Docker, Kubernetes and Python.

Key Responsibilities:

  • Modeling of a variety of logistic industry problems
  • Develop new (and improve existing) machine learning algorithms
  • Implement, release, and then maintain the algorithms running in production
  • Implement APIs to serve these models in production
  • Analyze metrics and look for improvement opportunities
  • Implement data Pipelines and ETLs
  • Work in optimization problems and mathematical modeling

Qualifications:

  • Communication. You are comfortable talking (and writing) to native and non native English speakers on a daily basis - you will work in an international team with native and non native English speakers.
  • 4+ years of experience as a machine learning engineer or data scientist.
  • Ability to lead the implementation of machine learning models from problem definition to serving it in production.
  • Passionate about creating clean, highly maintainable, and structured code for these models, implementing them as software and not only notebooks analysis.
  • Curiosity. You are keen on learning new methodologies, technologies and tools; also, on evaluating their pros and cons. You ask questions; always eager to learn more. But you are also pragmatic.
  • Leadership. You have mentored others and helped them become the best version of themselves. You are a reference to the group.
  • Experience with A/B testing.
  • Experience with exploratory data analysis.
  • Experience working with relational and non-relational databases.
  • Understanding of how Databases work and ability to implement REST APIs to serve machine learning models.
  • Experience modeling and implementing machine learning algorithms (either from scratch or by using libraries / frameworks).
  • Deep Computer Science knowledge and combinatorial optimization knowledge are a plus.
  • Experience with AWS, Kubernetes and terraform are a plus.
  • Knowledge of automated testing is a plus.

What you will find here:

  • Generous Stock Option Plan
  • Competitive Compensation
  • Building a Rapidly-Growing Tech Company
  • International Environment / Career
  • Ability to Work with Cutting-Edge Technology
  • Access to an Online Learning Platform
  • Mind and body initiatives: work out platform, yoga classes, walking challenges


Summary
Company: Loadsmart
Job title: Senior Machine Learning Engineer (Remote-LatAm) at Loadsmart () (allows remote)
Job tags: machine learning, data science, pandas, sql, data analysis




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