Klaviyo is growing fast and we have openings for data science interns! 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.
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.
The ideal candidate has a quantitative background, experience programming in Python, and is interested in doing everything from exploratory analysis to training and deploying models. We use a wide variety of data mining and machine learning algorithms. 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:
- Take on a project that will make a meaningful, real impact for Klaviyo users or internal company stakeholders
- 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 an understanding of 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 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.
- Pursuing a degree in statistics, applied mathematics, computer science or other relevant quantitative discipline.