Principal Data Scientist
Remote - Anywhere in the US
Heap is built on a rich dataset of product activity, encompassing hundreds of billions of events across thousands of websites. We’ve automated the process of capturing user behavior, and now we’re now building the next generation of tools to help clients understand and take action on their data.
In this role, you’ll research, develop, and productize advanced features for Heap’s product analytics platform. These features will guide users’ attention towards “unknown unknowns” in their data, suggest the right questions to ask, and otherwise decrease the barriers to insights. This is an opportunity to scale your data science knowledge across hundreds of companies: instead of discovering important business insights for one company at a time, here you can turn your knowledge into a product that helps thousands of product managers at once.
You’ll iterate quickly on challenging and interesting technical problems. How can we turn an autocaptured dataset of millions of events into quantifiable insights? How can we automatically identify frustrating aspects of a customer’s product? How can we help our clients avoid overfitting, and protect them from being fooled by confounding factors?
As Heap’s second data scientist, you’ll work directly with our CTO and alongside an expert data scientist and engineer on a fast-moving team. As you develop these methods, you’ll partner directly with our clients to test your approaches on a variety of datasets. This project is central to Heap’s future strategy and to our continued success as the leading product analytics platform.
What you will be doing:
- Research and develop novel product features. You’ll be solving challenging technical and statistical problems that automate insights for Heap users.
- Test prototypes with clients. You’ll pair directly with clients to understand their data and business problems, in order to test methods and product features in practice.
- Productize and scale. You’ll partner with our engineers, designers, and product managers to turn new ideas into practical and statistically rigorous features in the Heap product.
- Level up Heap’s data science capabilities. We’re developing data science as a core competency of Heap. As an early and senior member of the team, you’ll help build and scale our technical infrastructure and lead an initiative that’s key to the company’s long-term strategy.
What we’re looking for:
- 4+ years of professional experience as a data scientist
- Expertise in either R or Python. You’re fluent in transforming, modeling, and visualizing data to solve a variety of problems.
- Comfortable in SQL. Experience with data warehouses such as Snowflake, Redshift or BigQuery is a plus.
- Experienced with statistics and machine learning. You’re familiar with dangers like overfitting, confounding factors, and multiple hypothesis testing, and you’re well-versed in statistical methods for handling such risks.
- Practical: you care about finding effective and robust approaches that can be scaled into products, not about using the newest and shiniest technologies.
- An advanced degree (PhD, MS) in a quantitative field is optional but preferred.
- Strong communication skills: able to collaborate with technical, nontechnical and external stakeholders, including presenting conclusions to clients.
- You prefer rapid iteration, and have an interest in proving out ideas and making decisions in as fast a loop as possible.
- You can have a lot of fun playing with data.
People are what make Heap awesome. Regardless of age, education, ethnicity, gender, sexual orientation, or any personal characteristics, we want everyone to feel welcome. We are committed to building a diverse and inclusive equal opportunity workplace everyone can call home.
Heap has raised $95M in funding from NEA, Y Combinator, Menlo Ventures, SVAngel, Sam Altman, Garry Tan, Alexis Ohanian, Harj Taggar, Ram Shriram, and others. We offer plenty of awesome benefits, and we were named #1 on Glassdoor’s Best Places to Work (SMB). We'd love to hear from you!