# Data Science At Asana: A Day in the Life > Data Science At Asana: A Day in the Life Source: https://asana.com/inside-asana/data-science-day-in-the-life ## Data Science At Asana: A Day in the Life As the sun sets on my first year as a data scientist at Asana, I find myself immensely grateful for a workplace that continually challenges me and propels my growth. In the past year, I’ve learned how to build strategic analyses, update production data tables, run experiments, and track metrics for partner teams. While I anticipated growth in the technical aspects of my role, I quickly uncovered an unexpected dimension – communicating data to stakeholders in a way that leads to actionable product improvements. This fascinating intersection between analytics and storytelling shapes most of my projects at Asana. While each workday is filled with various tasks, they all converge on a singular goal – leveraging data to deliver valuable insights that drive our product forward. Here’s what a typical workday in my life looks like: #### **A Day in the Life** #### **Arrive at Office** I arrive at the San Francisco office and head straight to Scratch Kitchen for breakfast with coworkers. After grabbing a cardamom latte from Velocity Cafe, I head to my desk to begin my work day. I typically start by checking Asana, Slack, and Google Calendar to plan what work I’ll be prioritizing that day, as well as which meetings to prepare for. #### **Work Block 1: A/B Testing** My first task of the day is running an A/B test for my team. An A/B test involves presenting two versions of a feature to different users and collecting data to determine which one performs better. These experiments shape the team’s product strategy by providing a clear signal on which version of the feature to publish. My experiment has just concluded, so I am in the ‘analysis’ stage of A/B testing. This involves comparing the performance of version A and B using our predetermined target metrics, summarizing key results, and outlining next steps for the team based on the experiment findings. Next, I head to a room for my first meeting of the day – a post-experiment sync with the product manager and engineer on my team. As the host of this meeting, my goal is to present the key findings from the A/B test and drive alignment on which version of the feature to deploy in the product. This is where communication becomes critical in data science. While analyzing data is essential, it is equally important to synthesize findings into a clear and actionable story for stakeholders. These stakeholders range from product managers and engineers to UX researchers and designers – anyone looking to make product decisions. Through my experience with several projects at Asana, I’ve learned that the quality of my work hinges on its value to others, and honing the skill of communicating that value effectively remains a key component of my growth as a data scientist. #### **Team Lunch** After a productive meeting with the project leads, I head downstairs to grab lunch with my team. On today’s menu: cumin sichuan lamb and chicken asado torta with corn salad! #### **Jam Session: Data Analysis** After lunch, I shift focus from experimentation to a larger project – a deep-dive analysis of a specific user flow. These analyses typically start with a broad, high-level question from stakeholders, such as, “How do we improve this user flow?” As data scientists, we combine analytical expertise with product intuition to break this question down into specific points for investigation, exploring actionable recommendations to drive product directions and roadmaps. We then embark on exploratory data analysis to develop these recommendations. One of the most valuable lessons I’ve learned at Asana is to avoid working in isolation – collaboration significantly enhances the quality of your work. Feedback from stakeholders and peers, especially in the early stages, is crucial for identifying areas with the highest potential impact. Today, I’m hosting a jam session with a fellow data scientist to get early feedback on my current project and begin drafting a story from the insights I’ve gathered so far. Through collaborative meetings like this, I feel empowered to deliver the best and most impactful version of my work. #### **Work Block 2: Dashboarding** Afterward, I dive into my final task of the day: building a dashboard for my team’s upcoming feature launch. Supporting my partner team with data is a key part of my role – whether that involves sizing opportunities for potential projects, setting quantitative goals, or tracking success metrics for feature launches. This week, my team is rolling out a major feature and I am responsible for creating a dashboard that will track and showcase key usage metrics. This dashboard will enable the team to monitor delivery to users and assess the overall success of the launch. My tasks include identifying and designing relevant metrics to display, querying those metrics, and building the dashboard in a way that’s informative and easy to understand for everyone. I spend the rest of the day focused on this project until about 5:30 PM, when I wrap up for the day. #### **Signing Off** In the past year, my role at Asana has taught me to balance and embrace both the technical and collaborative aspects of data science. Harnessing these skills to deliver recommendations that enhance our product is challenging, yet rewarding. Above all, I am grateful for a workplace that encourages my ongoing journey of growth, and I look forward to continuing on this invigorating path at Asana. If you’re interested in learning more or joining the Asana team [visit here](https://grnh.se/44a30d851us). _About the Author_ _Edrea Low is a data scientist in our San Francisco office. As part of the data science team, she collaborates with product, engineering, and design teams to deliver data-driven recommendations that enhance our user experience._ - [我們如何提升無障礙測試的等級](/zh-tw/inside-asana/how-we-leveled-up-accessibility-testing) Asana 團隊 #### 內容撰稿人 在我們的協助工具旅程初期，我們通常將協助工具測試分為兩個類別：自動化和手動。 前者用於快速發現可透過演算法偵測的容易解決的問題，後者則用於需要更多時間和專注力才能找到的所有其他問題。 自動化測試非常適合持續整合 (CI) 等快速流程，而手動測試則需要與熟練的測試人員仔細協調，以涵蓋自動化工具無法處理的所有內容。在此過程中，我們發現這兩種方法之間其實存在一個 ... - [認識 Gradient：Asana 的多元文化員工資源小組](/zh-tw/inside-asana/gradient-multicultural-erg) Asana 團隊 #### 內容撰稿人 今年在 Asana，我們認識到需要不斷發展的策略來營造歸屬感。 因此，我們決定在過去一年中建立兩個新的員工資源群組 (ERG)——Blacsana，我們的黑人 Asana 成員 ERG，以及 Asanapac，我們的泛亞洲和太平洋島嶼社群 ERG。 這些新的 ERG 讓人們對 Gradient ERG 在組織內可以發揮的作用產生了好奇心。 因此，Gradi ... - [Data Science At Asana: A Day in the Life](/zh-tw/inside-asana/data-science-day-in-the-life) Asana 團隊 - [內容撰稿人](/author/team-asana) As the sun sets on my first year as a data scientist at Asana, I find myself immensely grateful for a workplace that continually challenges me and propels my growth.In the past ye ... - [Asana 團隊](/inside-asana/team-asana)