Staff Data Scientist, Data Products
New
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SyndioCompensation Intelligence
Remote Home Office - United StatesFull-TimeStaff
Salary$180,000 -$205,000 per year
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonSQLProduct AnalyticsData scienceBigQueryR
Requirements
- 8+ years of experience in product analytics, data science, or analytics engineering.
- Substantial experience supporting software products or developing analytical products.
- Deep proficiency in SQL.
- Strong working proficiency with Python, R, or a comparable analytical language.
- Advanced knowledge of statistical measurement, benchmarking, experimentation, and data modeling.
- Experience designing event schemas, product measurement strategies, and self-serve reporting.
- Proficiency with Mixpanel, Amplitude, Looker, Tableau, or equivalent tools.
- Experience combining large, imperfect datasets into commercially valuable products or insights.
- Proficiency with AI-assisted development tools and workflows.
- Familiarity with emerging AI and BI innovations like natural-language interfaces and agentic workflows.
- Ability to influence senior stakeholders and drive alignment across technical and non-technical audiences.
Responsibilities
- Own measurement strategies for core user workflows, defining metrics and success criteria to guide product decisions.
- Partner with engineering to design and validate event instrumentation for new features.
- Conduct deep-dive analyses into feature adoption, user engagement, and operational patterns.
- Build and maintain production-grade dashboards and self-serve reporting in Mixpanel, BigQuery, and BI tools.
- Translate complex usage data into prioritized recommendations for technical and executive audiences.
- Define and lead analytical strategies for new compensation and workforce data products.
- Identify opportunities to combine Syndio's data with market, job-posting, and skills data.
- Develop diagnostics for compensation governance and execution health.
- Lead complex initiatives from exploration through productionization and commercialization.
- Provide technical leadership, raise data literacy, and champion standards for analytical rigor and privacy.
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