OVERVIEW

Instacart’s core product is grocery delivery. Grocery delivery relies on high quality shoppers picking, paying for, and delivering grocery orders to customers. The Shopper team builds the technology to power each of these steps with the Engagement team focusing on maximizing batches fulfilled by high quality shoppers. 

ABOUT THE JOB

You would be responsible for driving Engagement data science work which includes:

1. Product strategy: Using data to identify the most promising opportunity areas along with contributing data expertise to refine and develop new product ideas
2. Impact sizing: Providing an estimated impact of implementing a new feature
3. Impact measurement: Running an experiment to determine the actual impact of newly launched product features
4. Intelligent systems: Improving a product feature by adding intelligent functionality to the product that relies on data science methods including statistical and machine learning methods

The two most important responsibility areas of this role are product strategy and intelligent systems.

Product strategy: The best products are built with diversity of thought. Data scientists uniquely understand the available data, the business domain, and the advanced methods that can be applied to the data to drive the desired business outcome. There are two primary methods data scientists would add value in product strategy:

1. Clearly articulate the right problem to solve to the team by providing a data-driven understanding of the problem area with the team as well as convincing the team of the most promising opportunity areas within the problem space
2. Contribute product ideas that leverage intelligent systems to solve the problems identified while collaborating closely with product, research, design, and engineering teams on the overall solution.

Intelligent systems: Intelligent systems make products smarter by combining deep domain expertise with advanced statistical or ML approaches. Data scientists are uniquely positioned to combine their understanding of the domain and advanced statistical methods to rapidly iterate on different approaches to building an intelligent system to solve an ambiguous problem. They would invest time in creating the initial version of the intelligent systems while partnering with MLEs and SWEs to maximize the impact, scalability, and maintainability of the system.

The role requires strong XFN collaboration where an ideal candidate can:

1. Drive critical efforts to completion with little oversight, while occasionally jumping into roles adjacent to data science (i.e. data engineering, machine learning engineer, etc).
2. Bring new ideas to the team that get incorporated into the product roadmap
3. Ruthlessly prioritize among requests from multiple competing stakeholders
4. Act as a senior cross-functional leader of the team, aligning the team on principles, processes and goals, and mentoring junior colleagues

About the Team

You will be joining a growing data science team and will tackle some of the most challenging and impactful problems that are transforming how people buy groceries every day. You will be embedded within our data-driven product team as a trusted partner in uncovering barriers in the product’s usability and utilize these insights to inform product improvements that drive angle-changing growth. We’re looking for a self-driven, strategic thinker who can hit the ground running to ultimately influence decision-making across the entire organization.

ABOUT YOU

Minimum Qualifications

• 6+ years of work experience in a data science or related field
• Led a team to adopt at least one new metric with which to measure the performance of the team
• Ran several sophisticated A/B tests at a company
• Built a machine learning or statistical model that made its way into production even if the code you wrote was completely rewritten before being deployed
• Experience with Python (pandas), SQL, git, and Jupyter notebooks

Preferred Qualifications

• 8+ years of work experience in a data science or related field
• Led a cross-functional team to design a system of metrics that connected the business strategy to a set of core metrics and assigned ownership of those metrics to the team
• Significant causal experimentation experience in environments with small sample sizes  
• Built a complex machine learning or statistical model and deployed/supported it in production
• Experience with Python (pandas; scikit-learn, statsmodels, or PyTorch; a visualization library that is not matplotlib; a web framework such as flask), SQL (Snowflake), git, Jupyter notebooks