AI News, Data Science at Instacart
- On Sunday, June 3, 2018
- By Read More
Data Science at Instacart
We also give flexible work opportunities to thousands of personal shoppers, and we extend the reach and sales volume for our hundreds of retail partners.
Our site and app are intuitive — you fill your shopping cart, pick the hour you want delivery to occur in, and then the groceries are handed to you at your doorstep.
Our fulfillment algorithm decides in real time how to route those shoppers to store locations to pick groceries and deliver them to customers’ door-steps in as little as one hour.
We have to balance speed (some shoppers shop faster, some stores are less busy) with efficiency (can we deliver multiple orders simultaneously) with quality (does the customer get the exact groceries they want) and timeliness (is the order delivered within the hour it is due — no earlier, no later).
Optimizing multiple objectives while routing thousands of shoppers every minute to fulfill millions of orders is a tremendous data science challenge.
Balancing supply and demand requires sophisticated systems for forecasting customer and shopper behavior down to individual store locations by hour of day many days into the future.
We then create staffing plans that blend multiple different labor role types to optimize our efficiency while ensuring high availability for our customers.
Then in real time, we have to estimate our capacity for orders every time a user visits our site or one of our apps, and then dynamically control availability and busy pricing to smooth demand and create the optimal customer experience.
These systems operate over multiple time horizons, have to solve for multiple competing objectives, and control for many erratic sources of variation (shopper behavior, weather, special events, etc.).
Through investments in search and personalization, Instacart has the opportunity to go beyond convenience in shopping online, and into a future where everyone finds more food they love faster.
We have made the conscious decision to embed our data scientists into our product teams, side-by-side with their engineers, designers and product managers and reporting into the engineering leader for the team.
As the VP of data science, it’s my job to make sure that the data scientists stay connected, have the mentorship they need, and are having the biggest impact they can within their teams.
Their ideas can directly shape not only product innovation, but also data collection and infrastructure innovation to fuel future product ideas.
This lets data scientists put new ideas into production in days (from inception), and to rapidly iterate on those ideas as they receive feedback from their consumers.
Our values form the corner-stone of our culture, and these in particular are key for hiring data scientists: Customer Focus We seek to understand the problems we work on as holistically as we can, and to reason through the physics of the system and how our many constituents (consumers, shoppers, our partners) will experience the changes we drive.
Highest Standards With ownership and a mandate for urgency comes a great responsibility — we must maintain the highest standards possible for the work we produce, as it has the potential to impact millions of consumers, thousands of shoppers and hundreds of retail partners.
We look for exceptional candidates who can do amazing work, and are always seeking better ways — be they new algorithms, new processes or new implementations.
Ensuring our eyes are wide open to these ideas, and that we collaborate openly within our teams and are always open to questioning our biases and assumptions is critically important.
- On Saturday, December 7, 2019
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