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00:00:00 – 00:38:27
The video delves into Anahita Tasi's insights as the Vice President of Data Science at Instacart, emphasizing the challenges of achieving positive unit economics in grocery e-commerce. Instacart's unique four-sided marketplace model involves consumers, shoppers, retailers, and brand advertisers. The discussion covers how Instacart organizes its data science team to address challenges and enable objectivity, functional excellence, and career development. The importance of virtual teams, data-led business strategy, advanced experimentation platforms, and data infrastructure investments are highlighted. The video also explores the team's evolution, role specialization, creating clear job expectations, and the merging of data science and business operations to drive efficiency and adaptability. An emphasis is placed on leveraging data science to enhance the personalized shopping experience while navigating the rapidly evolving macroeconomic environment. The overarching theme underscores Instacart's success, particularly during the pandemic, attributing it to the critical role of data science within the company.
00:00:00
In this segment of the video, the host introduces Anahita Tasi, Vice President and Head of Data Science and Business Operations at Instacart. Anahita discusses her career path and experience at Instacart, highlighting the challenges of achieving positive unit economics in grocery e-commerce. She explains Instacart’s unique four-sided marketplace model involving consumers, shoppers, retailers, and brand advertisers. An example of a challenge she mentions is balancing the needs and experiences of both shoppers and consumers through data-driven decision-making.
00:05:00
In this part of the video, the speaker discusses how at Instacart, they organize their data science team to address challenges and enable objectivity, functional excellence, and career development. The team is structured to align with different sides of the marketplace – Consumers, Shoppers, Res sers, and advertisers. They utilize cross-functional teams focusing on specific areas like search experience. Additionally, they have implemented virtual teams to tackle complex problems spanning multiple pillars, promoting expertise and collaboration. Product managers at Instacart have self-serving tools for common questions and strategic insights, and data drives decision-making during planning processes. Weekly business reviews are used to review key trends in data and impact decision-making. The speaker touches on the use of virtual teams, discussing the opportunities and drawbacks compared to a formalized organizational structure.
00:10:00
In this segment of the video, the speaker discusses the challenges and benefits of utilizing virtual teams. They highlight the need for clear ownership and operating models, making hard decisions about prioritizations, and empowering the team. The concept of virtual teams is emphasized to be essential in today’s cross-functional, customer-centric organizations. The discussion shifts towards Instacart’s business strategy being data-led rather than product- or sales-led. The importance of data in shaping decisions and the skills needed for a data science team, such as experimentation, causal inference modeling, and data analysis, are also highlighted.
00:15:00
In this segment of the video, the speaker discusses the use of an in-house experimentation platform with advanced techniques for testing and analysis. They emphasize streamlining reporting through clear dashboards for stakeholders’ self-service. The speaker also highlights recent data science initiatives, such as focusing on consumer habits and behavior to drive growth and developing tools to measure the effectiveness of advertising campaigns, which have led to stronger partnerships with advertisers. The speaker describes Instacart’s data-driven maturity as being in an exciting middle ground, balancing advanced tools and engineering with ongoing opportunities for impactful strategic findings and product development.
00:20:00
In this part of the video, the speaker discusses how their team leverages insights and analytics, powers product experiences, and supports machine learning engineering using a strong data foundation and infrastructure. They highlight their partnership with a data infrastructure team that invested heavily in building a world-class data platform. This platform, recognized by Snowflake, is built on Snowflake and DataBricks, focusing on data security, trustworthiness, usability, and extensive investments in areas like data governance and compliance. The speaker also shares their approach to hiring leadership teams, emphasizing traits like technical excellence, humility, curiosity, and the ability to build strong cross-functional partnerships.
Additionally, the speaker talks about how the team evolved as it scaled, transitioning from generalist data scientists who provide efficiencies due to reduced handoffs to more specialized roles such as marketing science, advertising, and statistics. This shift allows for a deeper focus on specific areas while maintaining a balance with generalist roles to answer a broader range of business questions and cater to individual interests and growth paths within the team.
00:25:00
In this part of the video, the speaker discusses the challenge of creating clear job expectations for data scientists with different roles within the organization. To address this issue, they defined different data science personas to acknowledge various flavors of data science work. These personas help guide career conversations, professional development, and org design. The speaker emphasizes the importance of investing in and growing top talent internally, providing consistent feedback, and being decisive in decision-making. Additionally, to improve efficiency during a downturn, the organization focuses on enhanced tooling, technology, knowledge management, AI, and stakeholder education. They also mention merging data science and business operations teams to streamline processes and clarify roles, leading to increased efficiency. These actions aim to drive efficiency and adapt to changing market dynamics in a rapidly evolving macroeconomic environment.
00:30:00
In this segment of the video, the speaker discusses the importance of clarifying roles and responsibilities within their team to eliminate redundancy. They emphasize the need to invest in the existing team, focusing on talent density and career growth. Despite maintaining a flat headcount, they have increased productivity and impactful work. The speaker also touches on using data science to enhance the personalized shopping experience on Instacart by understanding shopping patterns. The conversation concludes with praise for Instacart’s success during the pandemic and the critical role of data science in the company.
00:35:00
In this segment of the video, the speaker discusses how the organization is data-focused, with data engineering playing a crucial role in product planning. They emphasize the concept of data teams as growth teams, forming virtual teams for alignment around growth objectives. The organization has centralized data in a cloud data warehouse, utilizing tools like Snowflake and Databricks to analyze transactional and behavioral data for marketing insights. This approach enables personalized user experiences based on comprehensive data sources. The importance of virtual teams in decision-making and the need for organizational maturity to effectively implement such strategies are highlighted.