Lead Data Scientist (Classical ML & Statistical Modeling)

Randstad Malaysia; Kuala Lumpur; On-site; MYR 180,000 - 300,000; Full time

Job summary

Randstad Malaysia is looking for a Lead Data Scientist to drive analytics using traditional machine learning, statistical modeling, and predictive analytics. You will own the full DS lifecycle—from feature engineering to deployment—and mentor mid-level data scientists while communicating data-driven insights to executives.

The role requires 5+ years of experience, strong Python or R, and expert SQL skills, with a focus on maintaining rigor and delivering production-ready solutions.

Qualifications

• 5+ years of professional data science experience in traditional ML models.
• Strong Python or R and SQL for data prep and querying.
• Deep knowledge of supervised/unsupervised learning, regression, classification, and time-series.

Responsibilities

• Lead model development using classical ML methods and forecasting.
• Own end-to-end DS pipeline from data extraction to deployment.
• Mentor junior data scientists and communicate insights to stakeholders.

Skills

Python

R

SQL

Machine Learning

Statistics

A/B Testing

Pandas

NumPy

scikit-learn

LightGBM

XGBoost

Time-Series

Clustering

Docker

REST APIs

Education

Bachelor's or Master's degree

Tools

Docker

Job description

About the Role

We are seeking a Lead Data Scientist with strong expertise in core, traditional data science methodologies. In this role, you will lead our analytics initiatives by applying classical machine learning algorithms, advanced statistical modeling, hypothesis testing, and predictive analytics to solve critical business problems.

You will take technical ownership of the entire data science pipeline—from feature engineering and exploratory data analysis to model building, validation, and production deployment. You will also mentor mid-level data scientists and bridge the gap between technical execution and key business stakeholders.

Key Responsibilities

• Model Development: Design, build, and evaluate robust classical machine learning models (e.g., Logistic/Linear Regression, XGBoost, Random Forests, Time-Series Forecasting, Clustering) to tackle complex business problems.
• Statistical Analysis & Research: Conduct rigorous statistical testing, hypothesis testing, exploratory data analysis (EDA), and experimental design (A/B testing).
• End-to-End Pipeline Ownership: Own the complete data science lifecycle, including data extraction, cleaning, advanced feature engineering, model tuning, validation, and deployment into production workflows.
• Technical Leadership & Mentorship: Guide, review, and mentor junior and mid-level data scientists, ensuring high standards in code quality and statistical rigor.
• Stakeholder Collaboration: Translate ambiguous business requirements into technical data science roadmaps and present actionable, data-driven insights to executive stakeholders.

Requirements

• Experience: 5+ years of professional data science experience, with a proven track record of delivering traditional ML models to production environments.
• Core Technical Stack: Advanced proficiency in Python or R, and expert-level mastery of SQL for complex querying and data preparation.
• Machine Learning & Statistics: Deep theoretical and practical knowledge of supervised/unsupervised learning, probability, regression analysis, classification, and time-series models.
• Tooling: Proficiency with core analytical libraries (e.g., scikit-learn, pandas, numpy, statsmodels, LightGBM/XGBoost).
• Deployment & MLOps: Experience deploying predictive models into REST APIs or batch prediction pipelines using containerization (Docker) and ML pipelines.
• Education: Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline.

Nice to Have

• Master’s or Ph.D. in Statistics, Applied Mathematics, or a quantitative field.
• Experience with cloud analytics platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Experience working with big data tools (PySpark, Databricks, Snowflake).