Senior Product Analyst (CX)
New
J
JobgetherProduct analytics
Based in SwedenFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonMicrosoft SQL ServerMicrosoft ExcelData visualizationA/B testing
Requirements
- Higher education degree in mathematics, statistics, computer science, engineering, or another relevant technical discipline.
- Strong knowledge of mathematical statistics and probability theory.
- Proven experience applying statistical methods to real-world analytical and product problems.
- Advanced proficiency in data extraction, processing, analysis, and visualization.
- Hands-on experience using MS SQL, Python, and Excel.
- Deep practical experience designing, running, and analyzing A/B tests, including statistical evaluation and interpretation.
- Strong understanding of marketing and product metrics and their use in performance evaluation and strategic decisions.
- Ability to investigate complex questions, identify meaningful patterns, and translate findings into practical recommendations.
- Ability to collaborate with Product, Data Engineering, and functional leadership teams.
Responsibilities
- Own and evolve the product metrics tree, keeping key metrics consistently defined and connected to the north-star metric.
- Validate metrics and dashboards, identify inconsistencies, and specify reporting needs for Data Engineering.
- Investigate fluctuations, anomalies, and unexpected changes in business and product performance with functional and product leads.
- Research bottlenecks, conversion drop-offs, customer segments, and patterns that may reveal growth opportunities.
- Analyze product hypotheses and help Product teams prioritize problems by potential impact on key business metrics.
- Design and analyze A/B tests, including sample-size calculations, minimum detectable effect, duration, randomization splits, and pre-test validation.
- Check experiment splits and run sanity checks before interpreting results; report outcomes with appropriate guardrail metrics.
- Define experiment data-tracking requirements, including necessary events and instrumentation.
- Maintain an organized experiment archive and provide actionable recommendations from each test.
- Identify data-quality issues and inconsistencies before unreliable information informs decisions.
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