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- Design and implement data quality metrics, sampling methods, and audit frameworks
- Conduct statistical analysis to identify anomalies and ensure rating accuracy
- Build automation tools and self-serve dashboards to streamline quality checks.
Key Responsibilities
- Develop and refine predictive models to proactively identify and prevent quality issues.
- Define and track key quality metrics and KPIs to ensure data integrity and system performance.
- Perform deep statistical analysis to uncover trends, detect anomalies, and validate rating accuracy across large datasets.
- Build interactive dashboards and reports that make insights clear and actionable for stakeholders.
- Design and deploy self-serve tools that empower teams to run audits and access data independently.
- Partner with cross-functional teams to diagnose quality gaps and implement corrective measures.
- Conduct root cause analysis to resolve systemic data issues and improve reliability.
- Innovate and contribute to new QA processes and methodologies that raise the bar for data governance.
- Stay ahead of the curve by exploring emerging trends in data science, automation, and quality assurance.
- Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for data analysis and automation.
- Strong foundation in statistics and data quality principles not limited to (sampling, hypothesis testing, confidence intervals, risk analysis).
- Experience building self-serve tools or dashboards using frameworks like Flask or Streamlit.
- Familiarity with data governance, QA metrics, and risk management in data-driven systems.
- Develop & implement ML Models to predict/prevent quality issues
- Excellent communication skills and ability to translate complex insights into actionable recommendations.
- Bonus: Exposure to other advance statistical methods like Bayesian analysis, survival analysis, and exposure in ML/LLM systems.
- Opportunity to contribute to the quality and accuracy of products used by millions of users worldwide.
- Join a fast-moving, high-impact team working across data science, quality operations, and automation engineering.
- Work with cross-functional partners across data, search, and content domains.
EA Registration No.: YAP JUN WEI, R25126752
Allegis Group Singapore Pte Ltd, Company Reg No. 200909448N, EA Licence No. 10C4544
Key Skills
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