Our goal is to use machine learning to help companies make sense of their customers’ journeys and personalize each interaction in them. We use a mix of different approaches to accomplish that goal, including recommender systems, and regression and classification algorithms, implemented using a range of technologies, from neural networks to linear classifiers.
As a data scientist, you will help us build, test and ship models to power platform features and provide valuable results to our users. This involves helping with all stages of a machine learning pipeline, from data engineering to model evaluation.
You will work closely with the engineering team to ensure that our models scale in production to analyze vast amounts of customer data. You will also be expected to support our most experienced team members as they explore and apply novel modeling techniques to the problems that we tackle.
If this sounds exciting and you want to help build a product that leading brands use to delight millions of consumers, we’d love to hear from you!
What we’re looking for:
- Up to two years of industry experience or non-trivial personal projects with Python.
- Experience using machine learning to solve classification and regression problems, either professionally, in machine learning competitions (e.g. Kaggle) or in non-trivial personal projects.
- Experience with Python packages for data analysis and machine learning. Examples include numpy, Scipy, Pandas, scikit-learn, matplotlib, Seaborn, and Jupyter Notebook.
- Comfort working with databases, ideally MongoDB and/or MySQL.
- Comfort working in a Linux environment.
- Familiarity with recommender systems.
Bonus points if you have experience or familiarity with one or more of the following:
- Apache Spark
- Tools for building data pipelines such as Luigi and Airflow
- Deep learning libraries (e.g. Keras, TensorFlow, PyTorch)
- Gradient boosting algorithms (such as the models implemented by the xgboost and LightGBM frameworks)
Canopy Labs is an equal opportunity employer. We welcome applications from candidates from all backgrounds, including those under-represented in the technology industry. If you are contacted for an interview and require accommodation during the interviewing process, please let us know. Any information received relating to accommodation will be addressed confidentially.
To apply: Please send your resume to email@example.com. Please include links to any relevant projects and/or machine learning competitions you’ve taken part in.