Collect, process, and analyse large data sets from various sources.
Collaborate with product owners, data engineers, and partners across all product development stages - from concept and design to prototyping, testing, data curation, deployment, industry scaling, and end-of-life.
Collaborate with stakeholders to define data requirements and objectives.
Communicate findings and recommendations to non-technical stakeholders.
Perform exploratory data analysis to uncover insights and trends.
Design, build, and validate machine learning models and statistical algorithms.
Requirements
git
tableau
python
mlops
data science
team lead
Experience using Git for version control and familiarity with common collaboration tools such as GitHub or GitLab.
Strong knowledge of statistical methods and machine learning techniques such as regression, classification, clustering, time series forecasting, and anomaly detection.
Experience with data visualization tools like Tableau or Power BI.
Solid understanding of data wrangling, exploratory data analysis, feature engineering, and model evaluation, paired with the ability to select or define appropriate success metrics grounded in real business context.
At least 10 years of experience in a proven role as a data scientist or similar function, with at least 3 years in managing a team of data scientists, machine learning engineers and big data specialists
Familiarity with MLOps practices including model deployment, monitoring, and versioning of ML artifacts (e.g., MLflow, Airflow, integration with CI/CD pipelines).
Awareness of data governance, privacy, and security practices (e.g., GDPR, PDPA).
Excellent problem-solving skills and attention to detail.
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field.
Strong programming skills in Python (and/or R), with hands-on experience using machine learning libraries such as scikit-learn, XGBoost, TensorFlow, and PyTorch.
Strong storytelling, communication and collaboration skills.