Data Scientist

Intro to Emailage

Emailage is growing at an outstanding rate and we have an immediate opportunity for a skilled and enthusiastic Data Scientist to join our team.

Emailage is a global hub of intelligence associated with email addresses, which empowers companies to determine whether a transaction is fraudulent or legitimate.   This vast network is combined with machine learning algorithms to generate a Risk Score, based on rules that are easily tailored to existing risk engines.  Emailage Risk Assessment empowers companies to effectively fight fraud, increase revenue and transaction approvals, while preventing unnecessary customer friction.

Reports to Director of Data Science 



Data science and machine learning are the foundation of our approach to identify both risky and positive behaviours. We see it as the process of creating knowledge from data. At Emailage, we see a lot of data, and that will be your playground to implement scalable machine learning models and enhance big data processing capabilities in collaboration with our data scientists. You will be developing and deploying our advanced algorithms on production and perform as a bridge between data science and software development teams. You will also be expected to validate, document, and govern the models that our team builds.

Essential Functions

  • Implement and deploy large scale machine learning models to run on the cloud
  • Retrieve and manipulate very large imbalanced datasets and extract features
  • Validate and govern the model built, conduct statistical stability tests, and prepare model governance documentation
  • Design and develop big data enterprise solutions for data ingestion and transformation on AWS
  • Design data pipelines in production based on continuous delivery practices
  • Collaborate with data scientists to develop advanced modeling capabilities
  • Establish technical communication between software development and data science teams
  • Provide big data thought leadership to leverage Emailage’s data efficiently

Required Education and Experience

Master’s degree in Computer Science, Statistics, Mathematics, Operations Research, or related advanced quantitative field is required. PhD degree is preferred.

Additional Eligibility Qualifications

  • Ability to design highly performant systems and troubleshoot complex performance and scalability issues in production
  • Extensive theoretical and practical understanding of state-of-the-art supervised and unsupervised machine learning methods
  • Experience working with highly imbalanced datasets
  • 3 years+ of experience in working on and delivering research oriented data driven problems
  • 2 years+ experience munging data using R, Python and SQL
  • 2 years+ experience writing production code using C++, Java and/or Scala
  • 2 years+ of coding experience working with machine learning frameworks such as H2O, Tensorflow, MLLib, scikit-learn or similar tools
  • 2 years+ experience with AWS, specifically S3, EC2, and EMR
  • 2 years+ experience working with very large datasets using big data tools and platforms (Hadoop, Pig/Hive, Spark)
  • 2 years+ combined experience with the following technologies: Cassandra, DynamoDB, HBASE, MongoDB or other major NoSQL platforms
  • Ability to work in a dynamic, fast changing environment, with a strong attention to detail, communicate effectively and work well in a cross-functional environment

Company Benefits and Perks:

It’s no secret that we work hard, but we also strive to create an office environment where the lines between work and play are blurred. This means we offer these great perks to help keep our team healthy, productive and happy:

  • Health, dental and vision coverage
  • Life insurance, short-term, and long-term disability paid for by the company
  • 401(k) plan offered with employer match
  • High-end hardware to work with
  • Learning and career growth prospects
  • Paid Holiday and Time Off
  • Referral bonus program
  • Opportunities for profit sharing, bonuses and ownership
  • Ability to working with bleeding edge technology right here in AZ
  • Fast paced Startup Culture

About Emailage:

Emailage, founded in 2012 and with offices in Phoenix, London, Sao Paulo, Sydney and Singapore, is a leader in helping companies significantly reduce online fraud. Through key partnerships, proprietary data, and machine-learning technology, Emailage builds a multi-dimensional profile associated with a customer’s email address and renders a predictive risk score. Customers realize significant savings from identifying and stopping fraudulent transactions. To learn more, visit:

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