Staff Data Scientist (Mailchimp)
Published | November 29, 2022 |
Location | Atlanta, United States of America |
Category | Data Science |
Job Type | Full-time |
Description

Company Overview
Intuit is a global technology platform that helps consumers and small businesses overcome their most important financial challenges. Serving more than 100 million customers worldwide, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.
Job Overview
The Data Science team drives user growth and retention using product insights and marketing optimization for Mailchimp’s customers. We are an exciting, growing and fun team that works with industry-leading tools, techniques and best practices. We analyze millions of data points a day to make predictions, recommendations, automations, and differentiated experiences for our customers. We use cutting-edge machine learning models, advanced natural language processing and image processing to solve customer problems. Our data-driven tools help small businesses improve targeting and boost marketing and revenue using smart recommendations, create on-brand content, reach the right audience, and improve campaign performance. We’re a close knit group of 20+ Data Scientists where you’ll be recognized for your individual contributions to innovation and creative solutions. Yet, we find the best solutions come when we collaborate to solve them together. We bring equal rigor to our algorithmic prowess as our pie baking, ping pong, or pool games. As a member of our team you will be encouraged to bring your whole self to work and we value every idea and opinion.
As a Staff Data Scientist, you will dive deep into our customers marketing tactics to uncover actionable insights and make recommendations that will help our small business customers win. This position works alongside ML-Engineers, Product Management, Front-end Engineers, Analytics, and Strategy to deliver competitive business results and grow our customer base for our products. The Data Science team builds applications that make sense of the data created by Mailchimp’s millions of users. The ideal candidate is a self-directed, experienced data scientist who believes in demonstrating how data can be used to help our customers.
Qualifications
- 3+ years experience working on data science projects or applications at scale
- Outstanding communication skills with the ability to influence decision makers and build consensus with teams
- MSc or PhD in a relevant field (Computer Science, Statistics, Applied Math, Econometrics, Operations Research), or equivalent research experience from the industry
- Expertise in Data Mining algorithms and Statistical Modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, genetic algorithms, anomaly detection, recommender systems, sequential pattern discovery, or text mining
- Well versed in Data Science languages, tools, and frameworks, including data processing platforms and distributed computing systems (for example Python, R, SQL, SkLearn, Numpy, Pandas, TensorFlow, Keras)
- Strong experience with Cloud platforms (Google Cloud, AWS...)
- Ability to communicate methods and results of analyses.
- A unique background or expertise that gives you perspective into marketing, customer service, surveys, or design
- Experience with modeling in Ads Data Hub
- Experience with Twilio Segment
Responsibilities
-
Utilize Advanced Python skills to perform data segmentation
and aggregation from scratch; experience working with granular web clickstream data is needed.
- Develop personalized customer recommendations driving models including marketing plan forecasting and optimization.
- Develop behavioral propensity models aimed at optimizing users journey toward optimum outcomes for their business and Mailchimp as a whole.
- Collaborate with a cross functional squad of Product Managers, Engineers, Designers, and ML-Ops to accelerate model implementation driving product outcomes.
- Use data to influence product and tech decisions within your feature area.