Principle Scientist (Data Analytics)

Biocon; Gelang Patah; On-site; MYR 80,000 - 120,000; Full time

Job summary

A leading biopharmaceutical company in Malaysia is seeking a skilled Data Scientist to join its analytics team. The ideal candidate will possess 2–3 years of experience in machine learning and statistical analysis. You'll design and evaluate ML models, conduct data preparation and exploratory analysis, and communicate findings to stakeholders. Strong Python programming skills and a degree in a relevant field are required. This role offers opportunities to work with diverse tools and technologies, driving data-driven decisions.

Qualifications

• 2–4 years of experience in building, evaluating, and deploying ML models.
• Solid foundation in statistics including probability theory and regression analysis.
• Exposure to cloud platforms (AWS, GCP, or Azure) is advantageous.

Responsibilities

• Design, build, and evaluate supervised and unsupervised ML models.
• Clean, preprocess, and conduct exploratory data analysis (EDA) on large datasets.
• Monitor model performance and retrain or recalibrate models as required.

Skills

Machine Learning

Statistical Analysis

Python Programming

Data Preparation

Communication

Education

Bachelor's or Master's in Computer Science, Data Science, Statistics, or Mathematics

Tools

Python (NumPy, pandas, SciPy)

Machine Learning Libraries (scikit-learn, XGBoost, TensorFlow, PyTorch)

Data Visualization Tools (Matplotlib, Seaborn, Plotly)

Version Control (Git)

Job description

We are seeking a skilled and motivated Data Scientist with 2–3 years of hands‑on experience in machine learning and applied statistics to join our growing analytics team. The ideal candidate will have a strong foundation in statistical analysis, ML algorithms, model development, and deployment, with a passion for solving real‑world problems using data‑driven and statistically sound approaches

Responsibilities

1. Machine Learning & Statistical Modeling
   • Design, build, and evaluate supervised and unsupervised ML models (e.g., regression, classification, clustering, recommendation systems).
   • Apply statistical modeling techniques such as linear/logistic regression, regularization, Bayesian methods, and time‑series analysis where appropriate.
   • Perform feature engineering, model tuning, and validation using cross‑validation, statistical tests, and performance metrics.
2. Data Preparation, EDA & Statistical Analysis
   • Clean, preprocess, and transform large datasets from multiple structured and unstructured sources.
   • Conduct exploratory data analysis (EDA) using descriptive statistics, distributions, correlation analysis, and data visualization.
   • Use inferential statistics (hypothesis testing, confidence intervals, A/B testing) to support modeling decisions and business insights.
   • Evaluate models using both ML metrics (accuracy, precision‑recall, AUC, RMSE) and statistical measures.
   • Deployment of ML models into production using tools such as Flask, FastAPI, or cloud‑native services will be an add on.
   • Monitor model performance, data drift, and statistical stability; retrain or recalibrate models as required.
   • Work closely with data engineers, product managers, and business stakeholders to translate business problems into statistically and analytically sound solutions.
   • Communicate results, assumptions, and limitations of models clearly to both technical and non‑technical audiences.
3. Tools & Technologies
   • Use Python and libraries such as NumPy, pandas, SciPy, scikit‑learn, XGBoost, TensorFlow, or PyTorch.
   • Utilize visualization and analytics tools (Matplotlib, Seaborn, Plotly) for statistical reporting.
   • Leverage version control (Git), Jupyter notebooks, and ML lifecycle tools (MLflow, DVC).

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• 3–4 years of experience in building, evaluating, and deploying ML models.
• Strong programming skills in Python; working knowledge of SQL.
• Solid foundation in statistics, including probability theory, hypothesis testing, regression analysis, and experimental design.
• Exposure to cloud platforms (AWS, GCP, or Azure) and MLOps practices is advantageous.
• Excellent analytical thinking, problem‑solving, and communication skills.