Noah Daniels Email & Phone Number

Seattle, Washington, United States

Spending time working on passion projects during a brief hiatus to excel in my next opportunity.

San Francisco, California, Us

• Product Analytics: Directly served analytics and data reports to the product and operations teams to analyze trends and business decisions that directly impacted the insurance policy renewals process + servicing teams. Along with cross team collaboration and leadership, resulted in policy renewals becoming the number one driver of revenue in 2023 ($XXX M). Included analytics efforts towards project opportunity sizing, OKR planning, and tracking against financial forecasts.• Analytics Engineering: Restructured the data models in DBT consumed by Looker to provide better insight into the E2E lifecycle of quoting, renewals process, and servicing teams. This included a more transparent view on policy cancellations and extensions, a refined dataset for reviewing servicing metrics, and more. Also aligned Product, Engineering, and Data teams on a new approach to modeling Zendesk data which is now the source of truth for servicing metrics.• Reverse ETL: Pitched and led an initiative to integrate Census with Zendesk by leveraging data synced with Fivetran. This effort enriched Zendesk tickets with platform information, reducing the manual work needed for the servicing teams to start resolving tickets. Initial benefit realization analysis showed ticket resolution timea improving by ~30% after assignment for enriched tickets.• Stats/ML: Implemented a Cox regression with Kaplan-Meier curves to predict the likelihood of whether a policy would be renewed by 90d from coverage end date. The feature importance of the inputs to the model (Shapley values) validated assumptions about the highest impacts to policy retention and what aspects of the renewal teams should focus on to retain business. In addition, collaborated with the lead data scientist to transition a productionized ML risk model to renewals, incorporating new features and showing that it would not be a sufficient benefit to integrate.

Los Angeles, Ca, Us

Data Infrastructure: Primarily focused on supporting the build-out of the data infrastructure to provide more accessibility and ease-of-use for data across the organization. This included the migration of data warehousing from Redshift to Snowflake, setting up and managing ETL pipelines for big data streams (XX M rows, daily) coming from AWS Lambdas (using Snowpipe + DBT, Segment.io), and creating data models to serve end-users without the need to understand underlying data logic. Data Collection: Centralized data streams from integrations and microservices to track user behavior and create data foundation to support reporting & analysis goals using Segment. Oversaw internal tracking (client and server) by creating definitions for instrumentation and collaborating with engineers to ensure data was being collected as accurately as possible, and then QA testing the result. Data Science: Applied known ML algorithms (clustering, GBDT) that included using graph theory (community detection) and behavioral economics (auction theory). Informed on overall trends on ads within different channels and identified potential target audiences to help personalize advertising + reduce costs. Automated Reporting + Analysis: Processed requests from individuals (including executive team) to perform analysis and drove process around request workflows to the data team. Requests ranged from quality analysis, dashboarding, user analysis (LTV, segmentation), quarterly KPI projection, and more. Utilized FullStory for behavioral analysis and Chart.io, Snowsight, Tableau for dashboarding. Presented findings to executive team that are used to drive business decisions. Administration: Informed on overall data strategy, focusing on the data stack (tooling, data warehousing, etc.) and how it integrates with the current tech stack. Acted as DBA and owned data governance before onboarding a senior data engineer. Managed vendors for multiple services and data streams for the business

Los Angeles, Ca, Us

Joining the data team to provide actionable insights to the business through reporting and analytics.

Beaverton, Or, Us

Managing the code for a product similarity matching ML algorithm that serves 10 groups of business stakeholders across the North America, Europe, and Global divisions of Nike. This project drives key insights for the business by helping to create needed product assortments from available inventory and for forecasting the sales of new products in the market. Resulted in matching an additional 12mil units of inventory for assortments for the month September.Developed an automated process to identify and remove columns that contained similar information from the data in to support the similarity project described above and remove bias in the model. This process was presented to and implemented with another team that manages upstream data sources so that they can better govern their data.Partnering with key business stakeholders, solutions delivery teams, data engineers, data scientists, and other data analysts to produce internal solutions driven by data and led by business insight (using art + science). Evaluating and synthesizing results from numerous projects, and presenting findings to technical and non-technical audiences, including senior leadership teams. Supporting other data scientists by becoming familiar with the code and methodology behind the Nike Brand Revenue forecast for North America (pre-COVID) and demand planning forecasts. Measured the accuracy and validated outputs for demand forecasts to report to stakeholders and build trust in the forecast models.

San Francisco, California, Us

Defined success metrics for new features, including a metric for search success ("Kaggle Long Click"). Designed tracking instrumentation for engineers to implement and automated analytics pipelines between different data sources (SQL Server, Google BigQuery, Google Analytics) on GCP VMs. Communicated with engineering teams to understand how data was reported and relayed insights on user behavior to product teams through dashboards (Data Studio, Kaggle Kernels) and slide presentations.Built and automated ETL pipelines for public datasets with Python scripts which resulted in over 2000 datasets created (at one point was 14% of all public data on the platform) for a community of over 3MM data scientists. Data engineered the “Meta Kaggle” dataset (https://www.kaggle.com/kaggle/meta-kaggle) to be GDPR compliant, which is a public dataset of Kaggle user data that updates every 24 hours. In the first 3 months of my updates, saw an increase in user activity on the dataset by 50%.Designed a 2600 test QA plan for migrating the Kaggle platform from Microsoft Azure to GCP. Created a sitemap for reproducible testing across the site for all possible interactions with the platform, which included multiple user states and workflows. Iteratively tested each part of the platform, prioritizing issues in Google Sheets and Buganizer (Google's issue tracker). Consistently met with the Site Architect and the migration team about current testing progress and outstanding issues. Project managed the final stage bug cleanup pre-migration and reserved the right to decide if the platform was migration-ready.On my own time, trained a Latent Dirichlet allocation model for topic modeling Support tickets, offering holistic views on the most outstanding issues. Trained a multi-label classification model using linear support vector classifiers with cross-validation to categorize Support tickets and evaluate trends over a multi-quarter period, which were leveraged in product recommendations.

Eugene, Or, Us

I worked for the University of Oregon Academic Extension department as the Student Technical Support Staff. In this position, I was a primary response to staff support when it came to technical issues regarding anything from network connectivity to misbehaving software. Additionally, I worked with backup softwares, specifically CrashPlan and Retrospect, to manage the data of staff locally and abroad. I mainly worked with the OS X operating system and Apple machines (iMac, MacBook Pro, etc.) through Unix commands in Terminal and the GUI, but I've worked with Windows software and hardware as well. These were my main duties, in addition to other IT related projects that needed attention. I collaborated with other staff, including my supervisor, in order to maintain updated priorities on projects and responsibilities as well as to complete objectives effectively.

Eugene, Or, Us

Graded for various lower-level mathematics courses. Met with instructors to determine rubrics and in some cases, created my own based on the difficulty of the work assigned.

Eugene, Or, Us

As an Mathematics Undergraduate Teaching Assistant, I led a discussion section of about 30 students to help them learn concepts like logarithms and exponentials and how they can be applied to real life scenarios like personal financing. Additionally, I held office hours each week that were regularly attended and I graded homework for my section.

Minor, Business Administration And Management, General

Bachelor Of Science (B.S.), Computer And Information Sciences, General

Bachelor Of Science (B.S.), Economics