Data Scientist

Overview

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.

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.

Responsibilities

1. Data Architecture Infrastructure

• Review and refactor existing data table structures, field semantics, and key/ID systems to resolve legacy issues such as “one field with multiple meanings”

• Design and drive a unified data model and metric definitions; establish a company‑wide data dictionary and data standards

• Partner closely with engineering to participate in data warehouse modeling and data pipeline (ETL/ELT) design, improving data quality and maintainability

2. Experimentation System Evaluation Framework

• Own the overall methodology and implementation path for A/B testing and other online experiments across the company

• Design experiment pipelines, including traffic allocation, tracking/instrumentation strategy, data collection, data storage, and analysis workflows

• Develop standardized experiment analysis frameworks and reusable templates, including core metrics, significance testing, sample size estimation, and evaluation guidelines

3. Business Partnership Decision Support

• Deeply engage with core business lines and define problems and key metrics starting from product and business goals

• Based on the unified data system, provide structured data analysis and experiment recommendations to product and business teams

• Use data and experiment results to identify growth opportunities, product optimization directions, and potential risks, and clearly communicate findings to non‑technical stakeholders

4. Data Governance Best Practices

• Promote data naming conventions, field definitions, and tracking standards, and ensure their adoption across the company

• Establish and maintain data quality monitoring mechanisms to detect and fix data issues

• Document and socialize internal best practices related to data and experimentation, helping to build a strong data culture and improve overall data usage efficiency

Requirements

1. Education Experience

• Bachelor’s degree or above in Computer Science, Statistics, Mathematics, Information Engineering, or related fields

• 3+ years of experience as a Data Scientist, Data Product Manager, Data Engineer, or Growth Analyst

• Experience building a data system from scratch or leading large‑scale data infrastructure re‑architecture is a strong plus

2. Technical Skills

• Strong SQL skills: capable of handling complex joins and large‑scale queries with attention to performance and maintainability

• Proficient in Python (or a similar language) for data cleaning, analysis/modeling, and developing automated analysis scripts

• Solid understanding of data warehouse modeling concepts (e.g., dimensional modeling, star/snowflake schemas) and data architecture

• Familiar with online experimentation (A/B testing), including metric design, experiment design, statistical testing, and sample size estimation

3. Business Communication Skills

• Proven experience working closely with business teams and translating business problems into measurable, testable data and experiment questions

• Strong communication skills; able to collaborate effectively with product, business, and engineering stakeholders

• Highly self‑driven, with a strong sense of ownership for “making data and experimentation work well at the company” beyond just completing ad‑hoc analysis tasks

Nice to Have

• End‑to‑end experience building an experimentation platform, metrics platform, or data governance framework at the company level

• Experience in Internet / SaaS / consumer products, with practical work on user behavior analysis, funnel analysis, and retention analysis

• Familiarity with common statistical modeling or machine learning methods, with hands‑on experience in use cases such as recommendation, pricing, ranking, or user segmentation

We Hope You Are Someone Who

• Sees yourself not as a “report maintainer” but as the company‑level owner of data and experimentation systems

• Enjoys turning messy, fragmented data into something clear, unified, and reusable

• Wants your work to become foundational infrastructure that continuously supports company decisions and product iterations over the long term