Constructor is a new kind of search engine and product discovery platform built primarily on AI. It focuses primarily on ecommerce and powers some of the biggest ecommerce sites in the world such as Sephora, Petco, Under Armour, Birkenstock, Home24 and many more. . Constructors AI-first solutions make it easier for shoppers to discover products they want to buy and for ecommerce teams to deliver highly personalized experiences that drive impressive results. Optimizing specifically for ecommerce metrics like revenue, conversion rate and profit, Constructor generates consistent $10M+ lifts for some of the biggest brands in ecommerce. Constructor is a U.S. based company that was founded in 2015 by Eli Finkelshteyn and Dan McCormick.

The Experiments team at Constructor builds a platform to run A/B experiments and make data-driven decisions about product changes. We help other teams evaluate hypotheses and improve the quality of search, recommendations, and other services for our customers.

For this role, responsibilities are:

Expanding our knowledge of our experiments to improve their trustworthiness and convergence time by integrating state-of-the-art approaches for AB tests. This involves delving into customers datasets, deriving solutions that will work for different industries, business sizes, and shoppers’ patterns, and integrating them into our data pipelines;
Working with PMs and Engineers to scope the most important insights we expect to extract from our data and improvements that we’d like to bring to the experimentation process;
Working with a variety of ML/DS teams to foster best practices in their experimentation cycle and methodology - that includes helping them to analyze particular experiments to build common guidelines/platform improvements for streamlining such analysis;
Researching and implementing alternative approaches to data-driven decision-making.

What we expect:

You have a diversified experience in data/product analytics, including A/B experiments and other approaches to data-driven decision-making
You understand the underlying mathematical statistics and applicability limits of A/B testing and its alternatives
You’re able to defend your point of view in a reasoned and clear manner and listen to other peoples opinions
You are enthusiastic about improving data-driven culture in a growing company
You are proficient in SQL and Python for data analysis/data processing
(Optional) You will be a great match if you have experience with PySpark or ETL pipelines.