Machine Learning Specialist - Montréal, Canada - Local Logic

Local Logic
Local Logic
Verified Company
Montréal, Canada

1 week ago

Sophia Lee

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Sophia Lee

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Description

Your Mission


As a Machine Learning specialist, you'll be joining the Data team that is made up of data engineers, urban geospatial data experts as well as data science and machine learning developers.

The team is responsible for building end-to-end machine learning solutions to be integrated in Local Logic's next generation intelligence platform.

The platform is used to create our location-based insights and predictions from large and diverse sources of data.

It delivers value to our customers through APIs, some exposed externally, and some consumed by our Analytics Platform and our team of data scientists.

You believe in our mission and want to help us achieve it.

You bring your own unique perspective to the team, so you can challenge the way we do things for the better.

You're able to speak up when you disagree, ask questions when you don't understand, and take ownership of your work.


We're a remote-enabled company, so if you want to work remotely once a week, twice a week, or five days a week, you'll have all the necessary tools and support you need to do so.


Key responsibilities:


  • Analyzing and interpreting large and complex data sets using a variety of tools and techniques, including statistical analysis and machine learning
  • Developing machine learning models, sometimes starting with a prototype created by a data scientist or from scratch from a hypothesis and data
  • Working with product teams to identify customer needs that can be addressed using machine learning
  • Building and maintaining machine learning pipelines, including tasks such as data cleaning, feature engineering, and model training and evaluation
  • Implementing machine learning models in cloud production environments, including tasks such as deploying models to the cloud and setting up continuous integration and delivery (CI/CD) processes
  • Monitoring and maintaining machine learning models in production, including tasks such as monitoring model performance and retraining models as needed
  • Participate in establishing and maintaining MLOps best practices and tool selection

Requirements:


  • 5+ years of machine learning and data science experience; startup industry experience is a plus
  • Strong background in machine learning, statistics, and programming
  • Solid programming skills in Python with the ability to write production grade code that follows best practices
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikitlearn
  • Experience with data analysis libraries such as Pandas, Numpy, Scipy, etc.
  • Experience with data visualization libraries such as Matplotlib, Seaborn, Bokeh, Altair, etc.
  • Experience with cloud Infrastructure (we use AWS)
  • Familiarity with software engineering best practices and deployment tools
  • Excellent communication and collaboration skills

Additional Nice To Haves:


  • Experience with distributed compute engines such as Spark, Flink, Dask, etc.
  • Experience working with geospatial data with tools such as Apache Sedona, Geopandas, etc.
  • Experience with serverless architectures
  • Experience with infrastructure as code (we use AWS CDK)
  • Experience with orchestration tools such as Apache Airflow, Dagster, Prefect, or AWS Step Functions (We use Dagster)
  • Experience with MLflow or other MLOps model lifecycle management system
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