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Engineering, Full Time    Toronto, Ontario, Canada    Posted: Friday, August 23, 2019
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Company Overview is the open source leader in AI with a mission to democratize AI for everyone. is transforming the use of AI with software with its category-creating visionary open source machine learning platform, H2O. More than 18,000 companies use open-source H2O in mission-critical use cases for Finance, Insurance, Healthcare, Retail, Telco, Sales & Marketing. H2O Driverless AI uses "AI to do AI" in order to provide an easier, faster & cost-effective means of implementing data science. partners with leading technology companies such as NVIDIA, IBM, AWS, Intel, Microsoft Azure & Google Cloud Platform & is proud of its growing customer base which includes Capital One, Progressive Insurance, Comcast, Walgreens & MarketAxess. For more information & to learn more about how is driving an AI Transformation, visit

Job Summary:

  • Can you sling code proficiently in at least one language used by data scientists and/or data engineers, & does it excite you to learn more?

  • Are you skilled at predictive modeling?

  • Do you view communication skills just as important as technical ones?  

  • Can you listen to the needs of your peers & customers & adapt where need be? 

  • Do you have a competitive drive to be the best you can be?

  • Can you finish what you start?  

  • Can you own assignments given to you?

If the answer is "yes" to these questions, you potentially could be an excellent fit to join the team of customer engineering makers at  We deliver world-class solution experiences for our customers & drive revenue for our organization. Some of the technical projects you will work on include: training advanced machine learning models at scale in distributed environments, influencing next generation data science tools & data products, & pioneering ideas & products in new areas, such as machine learning interpretability, automatic machine learning, model management, deployment pipelines, & GPU computing.

Responsibilities & Duties:

As a Customer Data Scientist, you will be part of Customer Success team working closely with sales directors to:

  • Problem solve & assess technical problems, determine solutions, & work with internal engineering & customer teams to resolve them

  • Demonstrate ML solutions with engaging storytelling & technical accuracy

  • Architect, Design, & Deliver end to end machine learning workflows & systems from data ingestion to model deployment

  • Provide best practices & guidance to customers on machine learning workflows & systems from data ingestion to model deployment

  • Own account-related technical activities & relationships

  • Translate business use cases & requirements into technical ones

  • Communicate effectively to a diverse audience, including: engineers, business people, & executives.  Audiences will be large & small, & interactions will be in-person & online.

  • Drive field feedback back into product development & be very hands-on for all technical activities

Qualifications & Skills

  • Bachelor's degree in engineering, computer science, mathematics or a related field.  Graduate degree is a plus.

  • 2+ years experience with performing hands on Data Science & Machine Learning 

  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) & their real-world advantages/drawbacks.

  • visualization skills using R, Python or other languages & frameworks.

  • Knowledge of advanced statistical techniques & concepts (regression, properties of distributions, statistical tests & proper usage, etc.) & experience with applications.

  • 2+ years experience using statistical computer languages (R, Python, etc.) to manipulate data & draw insights from large data sets.

  • 2+ year working with data in Hadoop & /or Spark ecosystem

  • Desirable:  Maker mindset, coachable, & have an urge to learn/master new technologies Perks!

  • Flexible work hours & time off. is an equal opportunity employer. We welcome & encourage diversity in the workplace regardless of race, gender, sexual orientation, gender identity, disability or veteran status.

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