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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.
Responsibilities1. 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
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
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
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
Requirements1. 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
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
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
What We Offer
- Competitive salary and equity package, commensurate with experience and location.
- Flexible working hours and a fully remote work environment, with the ability to collaborate effectively across time zones.
- A dynamic and collaborative work environment that fosters innovation, growth, and professional development.
- The opportunity to work on cutting-edge technologies and help shape the future of AI, transforming industries and making a global impact.
