About Cardlytics

If you have a bank account or a credit card, chances are good that you’ve seen our platform in action. By running the cash-back rewards programs for Chase, Bank of America, Wells Fargo and other financial institutions we place targeted, relevant, measurable ads in front of 130 million US consumers - more MAUs than Twitter, Pinterest, and Snapchat. 

Cardlytics is the largest walled garden you’ve never heard of. We see one in every two card swipes across the US, covering $3.3T in purchases. This puts us in a unique position. We can help marketers predict consumer shopping preferences based on actual purchase data, and then use that data to reach bank consumers with offers they will love.

About the role 

In this role you will apply your experience in data science to build models that predict customer engagement and spending behavior. This is a great opportunity to join a growing team to build machine learning, AI, and deep learning solutions to power our platform with these main goals: 

• Leverage existing models to build fast and scalable machine learning solutions 
• Build model components that can be integrated with existing data pipelines and platform environment to build production level solutions 
• Innovate to find alternatives to accomplish goals and satisfy product requirements 
• Work in a fast-paced agile environment to deliver on time as part of a team building new products 

You will

• As a part of a team of data scientists work in Agile environment  
• Analyze large amounts of data to understand patterns and trends  
• Develop models to predict spending and engagement behavior 
• Participate in the design of testing methodology for models and pipeline and test model performance 
• Collaborate with other groups in Product and Engineering to understand goals and requirements 
• Work in a collaborative environment with other data scientists and software engineers to identify best practices and find best solutions. 

Skills + Experience 

Skills 

• Strong analytical and problem-solving skills 
• Experience with large data sets 
• Hands-on proficiency in SQL, Python and Linux shell, experience with Vertica is a plus 
• Demonstrated experience building machine learning models with frameworks including Scikit, TensorFlow, or similar packages. 
• Experience with time ordered data 
• Experience with code repositories 
• Experience building and running production level machine learning models (Docker, Kubernetes or similar framework) 
• Experience with Kafka, streaming data is a plus

Experience eiw

• At least 3 years of professional and/or academic experience as a data scientist 
• Experience deploying models to production 
• Master's degree in Mathematics, Statistics, Physics, Engineering, Decision Sciences, or similar field is a plus 
• Exposure to finTech and/or adTech is a big plus