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 takes 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. For this role, we’re looking for people who are strong at math, modeling, experimental design and putting themselves in the shoes of customers. We look for people who are interested in all aspects of what it takes to go from idea to generally available feature, even though your strengths and experience may 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 and deep practical experience with at least a few modeling and machine learning techniques.
You should have experience building models that are used by people to 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:
- Analyze large data sets (we’re collecting billions of individual actions every month).
- Build models and ship products that enable businesses to grow faster and communicate with their customers.
- Democratize and open up that technology to everyone.
- Match the right assumptions and models to the right problem.
- Measure and know what impact your models had on the decisions people made -- e.g. did they outperform the previous best model or a human decision maker?
Who you are:
- Possess a strong fundamental understanding and deep experience with at least some machine learning algorithms (e.g. regressions, decision trees, k-means clustering, neural networks).
- Understand Bayesian modeling techniques.
- Are capable of analyzing data and making rigorous statements about what can or cannot be concluded.
- Have experience designing and implementing model performance/validation assessments.
- Have a background in statistics and understand different distributions and the conditions under which they’re valid.
- Know how 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).
- Bachelor’s or advanced degree in statistics, applied mathematics, computer science or other relevant quantitative discipline, or equivalent industry experience.
Get to know Klaviyo
Klaviyo is a world-leading marketing automation platform dedicated to accelerating revenue and customer connection for online businesses using the channels they own like email, web and mobile. Enabling brands to leverage these owned marketing channels, Klaviyo makes it easy to store, access, analyze and use transactional and behavioral data to power highly-targeted customer and prospect communications. The company’s hybrid customer-data and marketing-platform model allows companies to grow by fostering direct, high-fidelity relationships with customers, without giving up their valuable data to Facebook or Amazon. In 2020, Klaviyo reached coveted unicorn status with a robust Series C of $200m at $4.15B valuation. Innovative companies like Unilever, Custom Ink, and Huckberry sell more with Klaviyo. Learn more at www.klaviyo.com.
Klaviyo does not tolerate and prohibits discrimination, harassment or retaliation of or against job applicants, contractors, interns, volunteers or employees by another employee, supervisor, vendor, customer or any third party.