• Partner with product, marketing, and operations teams to turn business needs into analytical solutions • Analyze large-scale customer and product data to uncover patterns, trends, and opportunities • Develop and refine customer segmentation models to support targeted marketing and personalization • Build and improve recommendation systems (collaborative, content-based, or hybrid) • Select, train, and evaluate machine-learning models with a strong focus on business outcomes • Define and track performance metrics for personalization and recommendation use cases • Support data quality, enrichment, and modeling during our migration to PIM and CDP platforms • Present insights and recommendations to stakeholders in a clear and actionable way

• Carefully parsing assigned machine learning papers or benchmark leaderboards and identifying key information. • Accurately extracting and compiling these technical details. • Producing informed estimates of metrics about the physical infrastructure supporting AI based on limited information. • Gathering data on the inputs, impacts, and details of AI, as well as carrying out analysis of the gathered data. • Reporting on and discussing findings with your supervisor.

• Assist senior algorithm engineers in the design and daily maintenance of search and recommendation services and models in content scenarios, support the content team and empower the development and iteration of the content ecosystem. • Support the optimization of content matching accuracy and distribution efficiency by applying machine learning-based personalization methods, common design patterns and tools, and participate in basic model tuning and effect verification. • Learn and understand the core logic of recommendation systems in the content domain, assist in the iteration of AI products to improve user content experience and core business metrics. • Participate in the auxiliary development and implementation of core content recommendation modules, assist in implementing data-driven strategies to maximize content value and user engagement. • Collaborate with content, business and product teams to identify needs and opportunities in content scenarios, and assist the team in defining core success metrics.

• Apply statistical analysis, data mining, and machine learning techniques to address operational needs • Develop, validate, and deploy predictive, descriptive, and diagnostic models in support of program objectives • Perform exploratory data analysis (EDA) to identify trends, patterns, anomalies, and risk indicators • Collaborate with data engineering teams to define data requirements, feature engineering approaches, and analytic pipelines • Ensure analytical methods and outputs are explainable, reproducible, and auditable • Develop and document model assumptions, methodologies, limitations, and performance metrics • Support use cases such as forecasting, risk scoring, anomaly detection, classification, and segmentation • Create visualizations, dashboards, and analytical summaries to communicate findings to stakeholders • Support validation, testing, and continuous monitoring of models to ensure ongoing performance and compliance • Produce high-quality documentation, technical memos, and briefings in accordance with federal standards • Participate in Agile/Hybrid delivery processes, including sprint planning, reviews, and status reporting