Chipotle Mexican Grill, Inc. (NYSE: CMG) is cultivating a better world by serving responsibly sourced, classically-cooked, real food with wholesome ingredients without added colors, flavors or other additives. Chipotle has more than 2,400 restaurants as of March 31, 2018 in the United States, Canada, the United Kingdom, France and Germany and is the only restaurant company of its size that owns and operates all its restaurants. With more than 70,000 employees passionate about providing a great guest experience, Chipotle is a longtime leader and innovator in the food industry. Chipotle is committed to making its food more accessible to everyone while continuing to be a brand with a demonstrated purpose as it leads the way in digital, technology and sustainable business practices.
CULTIVATING A BETTER WORLD
Food served fast doesn’t have to be a typical fast food experience. Chipotle has always done things differently, both in and out of our restaurants. We're changing the face of fast food, starting conversations, and directly supporting efforts to shift the future of farming and food. We hope you'll join us as we continue to learn, evolve, and shape what comes next on our mission to make better food accessible to everyone.
Chipotle is looking for innovative, experienced, and highly trained data scientist passionate about moving our company to the next level of being data driven and advancing our purpose. The Data Scientist will drive internal data analytics projects serving all areas of the company. Investigations will vary from short preliminary explorations to deeper, longer-term studies.
At Chipotle, you’ll have the opportunity to work side by side with talented, motivated, and intellectually stimulating colleagues who thrive on helping Chipotle solve our most pressing business opportunities.
WHAT YOU’LL DO
In addition to following Chipotle’s policies and procedures, principal responsibilities include, but are not limited to:
- Build the next level of data science capability at Chipotle from hardware and software to best practices to hands on deep diving analysis and models.
- Analyze existing data to generate insights for internal (marketing, product, coaching ops, member ops, sales) audiences.
- Partner with internal customers to define experimental questions and scope for each analytics investigation.
- Own top-notch, first-in-class computational science projects studying internal data from start to finish.
- Perform exploratory and targeted data analyses using descriptive statistics and other methods
- Translate data analytics outcomes into clear visualizations understandable to laymen.
- Report on analytics findings to senior leadership to inform key business decisions.
- Solve challenging analytical problems, which requires working with large and complex data sets and applying analytical methods including machine learning and statistical approaches as needed.
- End-to-end analysis including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
- Prototype and build analytical pipelines using various data sources to provide insights at scale.
- Interact cross-functionally with a wide variety of teams and work closely with them to identify opportunities to improve on platform.
- Developing analytical solutions, forecasting, and optimization methods to improve the quality of user experience. Areas include search quality, product recommendation, end-user behavioral modeling, user engagement, pricing, etc.
- Make business recommendations (e.g. cost-benefit, forecasting, experiment analysis) based on rigorous analysis.
- Work alongside software developers, software engineers and data engineers to translate algorithms into viable solutions
WHAT YOU’LL BRING TO THE TABLE
To perform this job successfully, you’ll need to be able to perform each essential duty. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Hold an advanced scientific degree (CS, Engineering, or [Applied] Mathematics), including training in statistical analysis and experimental design. Skilled in critically thinking through a research question, optimal methodologies, and clear operationalization’s of hypotheses.
- Expertise and hands on experience with statistical modeling and machine learning software (Python, R, etc.)
- Advanced knowledge of database query languages (SQL) and an understanding of relational algebra and set theory
- Comfort with common applied ML strategies and ensemble methodologies (e.g. train/test/validation sets, feature engineering, accuracy assessment).
- Experience building simple backend data infrastructure/pipelines. Know how to connect the dots between data sources (e.g. SaaS or other APIs), ETL/storage solutions (better if you’ve built these yourself), analysis environments (e.g. R or Python scripting), and (preferred but not required) front-end reporting (e.g. dashboards, apps).
- Strong knowledge of a variety of persistence layers, e.g. SQL (MS, Oracle), NoSQL (MongoDb, Neo4j), both on-prem, cloud, elastic or otherwise.
- Passionate about storytelling with data. Ability to port analytics outcomes into interactive- or presentation-based media (e.g. D3, Shiny, PowerPoint/Keynote).
- Understand technical basics of data visualization and common pitfalls. Ability to make clean, elegant, informative graphs (knowledge of ggplot2 visualization package).
- Exceptional written and verbal communication skills. Ability to present data science findings to sizable audiences of laymen and specialists alike.
- Outstanding leadership and organizational skills.
- Ability to work collaboratively as part of a fast-paced, customer oriented team
- Desire to use or create standards, consider reusability, and have a long-term view while understanding short-term needs
- Self-starter who provides ideas and solutions and understands various approaches within the BI discipline
- Restaurant or Retail industry experience (nice-to-have)