San Francisco, CA
At Afresh, we’re reducing food waste and saving money for grocery stores by more closely matching perishable food shipments with actual customer demand. Specifically, we use cutting-edge machine learning techniques (we’ve been published in ICML!) combined with an intuitive tablet-based ordering system to enhance the previously pen-and-paper-based grocery store ordering system.. The results are already commercially compelling: in live deployments in grocery store produce departments, we have demonstrated the potential to double profits and reduce food waste by 50%+!
What will you be doing?
- Integrating Data From Customers: incoming data is different at each grocery chain and comes in a wide range of formats (e.g. CSV files, Excel files, etc.). You will help to further develop our system for integrating this data into our Afresh databases.
- Supporting Production Data Processing: Supporting our production ETL processes to reliably ingest new data and make sure that our grocery customers are always notified with our most up-to-date order recommendations.
- Analyzing Data: Measuring and transforming the inbound customer data so that our Machine Learning team has the best possible data for training our ordering model.
- Visualization: Creating and maintaining dashboards and visualizations to support dialogue within our company and with our customers.
- Ad-hoc analysis: supporting the rest of the organization, including:
- Investigating data anomalies.
- Identifying relevant trends in the data that are not included in our model already, and your recommendations for a course of action.
What to expect in the job:
- Each day, you’ll figure out how to systematically solve the problems that are reported by our customers - both internal and external. You’ll conduct preliminary analysis, triage the problem, and determine who will work with you to resolve them. You’ll own the resolution of the problems: they’re fixed when you say they are.
- You’ll be responsive to ad-hoc assignments to prepare analyses, update Tableau workbooks and interact with customers to discuss and resolve technical issues.
- You’ll have longer, directed projects where you’ll be combing through our produce order recommendations and source data to identify trends, causes and cures. As part of this, you’ll create tools and processes that you and the rest of the team will use to improve our products and automate our work.
- You’ll develop new tools and find ways to automate existing manual or cumbersome processes.
What skills and experience do you need?
- The principal skills needed for this role are curiosity, scientific thinking and meticulous analysis.
- 3+ years of work experience, especially in a role using ETL or data pipeline technologies.
- We manipulate data in PostgreSQL; develop in Python, and build data visualization dashboards in Tableau. An ideal candidate would have mastery of one or more of these skills. However, candidates with any combination of the following skill sets will likely be a good match:
- Python (especially pandas, matplotlib, or numpy)
- R or SAS
- Tableau, Looker, or similar data visualization tool
- Excel (e.g. pivot tables, VLOOKUPs and SUMIFs)
- Airflow (DAG-based automation system)
- A general statistics background
About 30–40% of food produced worldwide is thrown away, causing nearly a trillion dollars of economic losses, trillions of gallons of wasted water, and billions of tons of additional greenhouse gas emissions. In the US, about 40% of all food waste occurs at the retail level and downstream, largely driven by insufficient technology and manual processes.
Afresh seeks to tackle some of these big problems around food waste. Born out of Stanford's Computer Science PhD program, Afresh is the first Fresh food supply chain company. We bring the cutting edge of artificial intelligence to Fresh food to minimize food waste.
Our machine learning-powered supply chain solutions are tailored for the nuances of perishables. Our first product is a store-level replenishment tool that optimizes the ordering of items in Fresh categories e.g. produce, meat, deli, dairy, bakery, and prepared foods. The goal is to minimize waste and maximize in-stock rate, and consequently, profit.
So far, the results are awesome! As mentioned above, in live deployments, we have demonstrated the potential to double profits and reduce food waste by 50%+.
We're growing fast: we're in partnership with 4 large regional grocers representing 500+ stores and >$10B in revenue. Our backers include Innovation Endeavors (former Google CEO Eric Schmidt’s firm) and Baseline Ventures (first money in Stitchfix, SoFi, Heroku, Instagram).
Interested? Email us at email@example.com