Ample Insight Inc.
We are looking for Data Scientists who are passionate about solving real world problems. You enjoy working with both structured and/or unstructured data and are motivated to productize scalable machine learning models. Critical thinking and problem-solving skills are essential for this role.
- BS (or higher, e.g., MS, or PhD) in Computer Science or related engineering field involving coding
- Experienced implementing and scaling machine learning models in production environments
- Strong understanding of machine learning theory
- Hands on experience with Statistics
- Capable of quickly implementing prototypes of cutting-edge research papers
- Proficient in Python (i.e. Pandas, Numpy, scikit-learn, etc), R, TensorFlow, amongst other data science related tools and libraries
- Analytical mind and strong business acumen
If you’re passionate about data science and is hungry to learn, please apply!
- We have competitive compensation.
- Work on cool projects based on your interests and skills. We believe in accountability and NOT micro-management.
You will join a world-class team of engineers and data scientists from Facebook, Uber, Amazon and Google. We are a fast growing consulting firm based in Toronto with clients ranging from leading startups building impactful technologies to Fortune 500 companies looking to scale their engineering and data capabilities.
Originally posted on Himalayas
To apply for this job please visit himalayas.app.
Working in Canada
Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, making it the second-largest country by total area, with the longest coastline of any country. Its border with the United States is the longest international land border. The country is characterized by a wide range of both meteorologic and geological regions. With a population of over 41 million, it has widely varying population densities, with the majority residing in its urban areas and large areas being sparsely populated. It
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