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Customer Data Scientist - North America 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 and Marketing. H2O Driverless AI uses "AI to do AI" in order to provide an easier, faster and cost-effective means of implementing data science. partners with leading technology companies such as NVIDIA, IBM, AWS, Intel, Microsoft Azure and Google Cloud Platform and is proud of its growing customer base which includes Capital One, Progressive Insurance, Comcast, Walgreens and MarketAxes. For more information and to learn more about how is driving an AI Transformation, visit

As a Customer Data Scientist, you will collaborate with Data Science teams on the customer side, work on diverse use cases from several industry verticals, and leverage domain expertise in data science and H2O platform to help customers achieve their AI objectives. This is an opportunity to work closely with some of the best engineering talent and the best data scientists/Kaggle Grandmasters in the world. 

This role may be based anywhere in North America.

What You Will Do

  • Enable customers to solve complex data science problems by providing consultation and guidance on use case identification, feature engineering, model selection and tuning, model deployment and optimization. 
  • Architect, design, and deliver Machine Learning and Data Science solutions. 
  • Demonstrate ML solutions with engaging storytelling and technical accuracy. 
  • Help customers understand model performance, model interpretability, and post deployment model monitoring concepts, and enable them to maximize power of the H2O platform, via training, workshop, and ongoing consultations. 
  • Act as a subject matter expert in H2O platform and data science.
  • Collaborate with Sales, R&D, and Product teams, to enhance H2O functionality 
  • Translate business cases and requirements into value based technical solutions through the architecture of machine learning workflows and systems from data ingestion to model deployment. 
  • Present at meetups and webinars in the Data Science community, and be an integral part of the Maker culture of creating the best products and solutions. 

What You Bring

  • Bachelor’s or a higher education degree in Computer Science/Engineering, Mathematics/Statistics 
  • Minimum 3 to 5 years of hands on experience solving data science problems in real world environment 

Data Science Skills

  • Experience with solving machine learning problems using H2O ML products (plus), Python, R 
  • Knowledge and experience of using a variety of machine learning techniques (supervised/unsupervised, clustering, decision tree learning, neural networks, etc.) and their real-world advantages/drawbacks/tuning techniques. 
  • Knowledge and experience of using advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) forpractical applications. 
  • Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc. 
  • Knowledge and experience of implementing end to end Data Engineering pipelines 
  • Experience of visualizing and presenting (EDA) to stakeholders using H2O Wave (plus), or other standard data visualization libraries in the Python and R stacks or using Tableau/PowerBi. 
  • Understanding and experience with post production model monitoring tools like H2O ML Ops (plus) MLFlow etc. 

Programming Languages & System Engineering Skills

  • Proficient in Python or R for data science. Java, Bash scripting, Scala Go are a plus 
  • Experience of distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, etc. 
  • Experience in Spark and/or Hadoop ecosystem 
  • Understanding of system engineering concepts and working in a linux based environment (OS fundamentals etc). Ubuntu and CentoOS/RHEL mainly. 
  • High level understanding of the cloud ecosystem, working with containers (docker etc) 

Additional Requirements 

  • Experience of working in a customer facing environment, providing data science consultation services 
  • Excellent communication skills. 
  • Amicable attitude. Aptitude to independently investigate and find solutions to problems; urge to learn/master new technologies. Maker mindset. 
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