Data Scientist
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Summary
A data scientist on the client team of a Malaysian recruitment agency designs real-time anomaly and pattern detection for transaction and platform data, builds predictive models for engagement, churn, and customer segments, and automates monitoring workflows using Python, SQL, ClickHouse, and AWS.
Job Responsibilities:
- Design and implement anomaly and pattern detection algorithms to identify irregular transaction behaviors, synthetic activities, and coordinated anomalies in real-time.
- Develop predictive analytics models to forecast user engagement trends, platform utilization, potential churn, and high-value customer segments.
- Automate manual data monitoring processes by building Python-based workflows and SQL-driven alert systems to detect and flag outliers instantly.
- Conduct in-depth data forensics investigations on complex and unstructured log data to determine root causes of discrepancies or unusual system activity.
- Collaborate with engineering teams to optimize data pipelines and structures (e.g., ClickHouse, AWS environments) to ensure high-performance model execution on live datasets.
- Write scalable, production-ready code and ensure model reliability, efficiency, and maintainability.
- Continuously refine analytical models and detection mechanisms to improve accuracy and reduce false positives.
Job Requirements:
- Bachelors Degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
- Minimum 1–2 years of hands-on experience in data science, analytics, or a data-intensive engineering role.
- Proficient in Python, with experience using libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, analysis, and modeling.
- Possessed SQL skills, including complex joins, window functions, CTEs, and large-scale data transformation.
- Foundation in statistics and machine learning concepts, including regression, classification, clustering, and outlier detection techniques.
- Experience with automation scripting and workflow optimization is required.
- Familiarity with containerization tools (e.g., Docker), high-performance databases (e.g., ClickHouse), AWS environments, or local AI models is an added advantage.
- Portfolio or GitHub repository showcasing relevant projects (data cleaning, ML models, automation scripts) is preferred.