Data Scientist

职位要求 / 描述

<div class="content-intro"><p><strong><u>About MyShell</u></strong></p> <p>MyShell is revolutionizing the AI landscape by building an open ecosystem for AI-native apps. Our powerful platform and intuitive toolkit empower anyone to create, access, and benefit from AI-powered applications. Launched in April 2023, MyShell has quickly gained global traction, attracting a diverse community of creators and users.</p> <p>Our team of talented individuals from top institutions like MIT, Princeton, and Oxford is committed to fostering innovation in a supportive and transparent work environment. With funding from leading VCs, MyShell is poised to reshape the future of AI, making it accessible and integral to everyone's daily life. Join us on this thrilling journey as we redefine what's possible with AI.</p></div><h3 data-start="36" data-end="57">Overview</h3> <p data-start="59" data-end="424">Our company is in a phase of rapidly building out our data infrastructure and experimentation framework. Our current data table structures, field definitions, ID systems, and experiment pipelines carry significant legacy issues: inconsistent metric definitions, fragmented experiment processes between engineering and business teams, and high decision-making costs.</p> <p data-start="426" data-end="721">We are looking for a Data Scientist with strong engineering skills, solid experimentation methodology, and deep business understanding to design and lead a unified data and experimentation framework across the company, enabling faster iteration and better decision‑making for all business lines.</p> <h3 data-start="728" data-end="748"><br>Responsibilities</h3> <h4 data-start="750" data-end="792">1. Data Architecture &amp; Infrastructure</h4> <ul data-start="793" data-end="1230"> <li data-start="793" data-end="950"> <p data-start="795" data-end="950">Review and refactor existing data table structures, field semantics, and key/ID systems to resolve legacy issues such as “one field with multiple meanings”</p> </li> <li data-start="951" data-end="1074"> <p data-start="953" data-end="1074">Design and drive a unified data model and metric definitions; establish a company‑wide data dictionary and data standards</p> </li> <li data-start="1075" data-end="1230"> <p data-start="1077" data-end="1230">Partner closely with engineering to participate in data warehouse modeling and data pipeline (ETL/ELT) design, improving data quality and maintainability</p> </li> </ul> <h4 data-start="1232" data-end="1285">2. Experimentation System &amp; Evaluation Framework</h4> <ul data-start="1286" data-end="1727"> <li data-start="1286" data-end="1403"> <p data-start="1288" data-end="1403">Own the overall methodology and implementation path for A/B testing and other online experiments across the company</p> </li> <li data-start="1404" data-end="1553"> <p data-start="1406" data-end="1553">Design experiment pipelines, including traffic allocation, tracking/instrumentation strategy, data collection, data storage, and analysis workflows</p> </li> <li data-start="1554" data-end="1727"> <p data-start="1556" data-end="1727">Develop standardized experiment analysis frameworks and reusable templates, including core metrics, significance testing, sample size estimation, and evaluation guidelines</p> </li> </ul> <h4 data-start="1729" data-end="1776">3. Business Partnership &amp; Decision Support</h4> <ul data-start="1777" data-end="2209"> <li data-start="1777" data-end="1894"> <p data-start="1779" data-end="1894">Deeply engage with core business lines and define problems and key metrics starting from product and business goals</p> </li> <li data-start="1895" data-end="2024"> <p data-start="1897" data-end="2024">Based on the unified data system, provide structured data analysis and experiment recommendations to product and business teams</p> </li> <li data-start="2025" data-end="2209"> <p data-start="2027" data-end="2209">Use data and experiment results to identify growth opportunities, produ

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