About Cardlytics

At Cardlytics (NASDAQ: CDLX), we created an industry…. but we are just getting started. From idea inception at a kitchen table in Atlanta to now publicly traded on NASDAQ with offices around the world, we are proud of the work we’ve accomplished and are looking for more smart and creative minds to join us on our mission.

By using data for good, we connect brands with real people through their banks’ digital channels (think: online and mobile apps). And when we make these connections, everyone wins - brands drive more sales, banks drive more loyalty, and people receive more cash back. It’s that simple.

So how exactly does it work?

Through our partnerships with financial services companies like Chase, Bank of America, Wells Fargo, and Venmo, we have insights into one out of every two card swipes in the U.S.. This equates to roughly $3.1 trillion in annual purchase spend from more than 161 million bank customers.

Respecting this complete view of the consumer, we can create mutually beneficial relationships between those consumers and their favorite brands and financial services partners. These are relationships that otherwise would not have happened without our help. It’s our advanced targeting that drives advertising performance - because performance matters, and it’s our superpower.

Are you ready to become a shareholder and join the engineering team filled with creatives and data scientists, consultants and artists, to help redefine marketing?

As a Principal Data Scientist / ML Engineer, you will:

· Design and develop a world-class Targeting user profiling models and pipeline

· Collaborate closely and build rapport with architect and engineering teams

· Take full ownership of production deployments while adhering to best practices for CI/CD

· Quickly prototype hypotheses

· Participate in design discussions, influence product roadmap, and take ownership and responsibility over new projects.

· Build highly scalable, available, fault-tolerant data processing systems using AWS technologies, Spark, and other ML technologies. These systems should handle batch and real-time data processing over 10s of terabytes of data ingested every day and petabyte-sized data warehouse.

· Low level systems debugging, performance measurement & optimization on large production clusters.

· Maintain and support existing platforms and evolve to newer technology stacks and architectures.

You have:

· Experience with machine learning, natural language processing solutions in software development.

· Experience developing and/or training models using machine learning technologies (e.g., TensorFlow/Keras, PyTorch).

· Direct experience in implementing, deployment, and optimizing ML workloads.

· Experience developing and debugging code in Python.

· Experience with exploratory data analysis, model development, and auxiliary practical concerns in production Machine Learning systems.

· Experience with deep learning architectures (e.g.,DNNs, Transformers, BERT, etc.).

· Ability to lead the design and implementation of AI-based solutions, web services, and debugging tools.

· Collaborate with cross-functional teams such as developers, analysts, and operations to execute deliverables.

· 5+ years professional experience as a data scientist.

· 3+ years of experience in a technical leadership role; overseeing projects.

· Master's degree or PhD in Computer Science or related technical field.

· Highly Preferred: Experience in machine learning of User Profiling system.

· Highly Preferred: Experience writing tools and pipelines for collecting, labeling, augmenting, and cleaning training data for user profiling ML models.

You are:

· Skilled and knowledgeable on user profiling techniques and advancements

· Passionate about the Targeting ecosystem, with willingness to learn about and adopt new concepts

· Excellent at problem solving through insightful data analysis and logical deduction

· A strong cross functional team player, working with product and architect to design and implement ML models.

· An effective communicator with a solution-oriented mindset.