Data Scientist Deliverability Compliance at Klaviyo

The Deliverability and Compliance team is in charge of protecting Klaviyo from phishers and other bad actors that want to abuse the platform. It’s also in charge of making sure the legitimate content gets through to recipients. Data science is being used to guide those processes and add intelligence to the way we vet accounts and content on the platform. You’ll sit alongside software engineers and product managers to directly improve the systems we have in production.
Klaviyo is growing fast and we have openings for all skill levels across data science. Listen to our data science team podcast at https://medium.com/klaviyo-data-science and learn more about our technical culture at https://klaviyo.tech 
Our data science team works on two types of projects:


Building features. We take ideas from inception to new machine learning-powered features that ship to our hundreds of thousands of users. You’ll be central to creating features that help our customers learn and grow from their data.


Support the business. We perform analyses, build models, and run experiments to make the Klaviyo business more efficient. You’ll work directly with stakeholders within the company and contribute directly to business processes at Klaviyo.


We’re looking for people who are strong at math, modeling, experimental design, and putting themselves in the shoes of customers. You should be interested in all aspects of what it takes to go from idea to generally available feature or internal solution, even if your strengths and experience today fall primarily in one area.
The ideal candidate has a background in data science, statistics and machine learning and has done work ranging from exploratory analysis to training and deploying models. We use a wide variety of data mining and machine learning algorithms. The right candidate will have both a solid fundamental understanding of and practical experience with at least a few modeling and machine learning techniques.
You should have experience presenting data to help people make better decisions. We’re focused on shipping early and often. We prefer iterative solutions that are incrementally better to the perfect solution.
How you will make a difference:

Build data science-powered features that our customers love and help them grow faster. Examples of features we’ve added to Klaviyo: A/B testing, personalized product recommendations, and creative assistance.
Use data science to help Klaviyo grow faster. This includes building smart algorithms to automate processes that are currently manual, performing analyses to help our internal teams know how to best target their resources, and building predictive models to better understand our customers and business.
Analyze large data sets (we’re collecting billions of individual actions every month).

Who you are:

Possess a strong fundamental understanding of  at least some machine learning algorithms (e.g. regressions, decision trees, k-means clustering, neural networks).
Capable of analyzing data and making rigorous statements about what can or cannot be concluded.
Know how to  design and implement model performance/validation assessments.
Have a background in statistics and understand different distributions and the conditions under which they’re valid.
Know how to code and have used data science tools and packages.
Have demonstrated a measurable impact based on the models you’ve created. It’s not always easy getting a model correct and we love talking about places we got stuck and working as a team to think through ideas that could unblock us.
Have a desire to ship features powered by data science (in other words, you’re excited by both upfront research and actually getting models into production at cloud scale); OR
Have a desire to work directly with business stakeholders to create tools that will provide insight and make them more efficient.
Bachelor’s or advanced degree in statistics, applied mathematics, computer science or other relevant quantitative discipline, or equivalent industry experience.