About the Role
We are looking for a Full-Stack Data Scientist to join our central analytics & ML team. In this role, you will own end-to-end development of machine learning solutions from problem discovery, data exploration, feature engineering, model development, deployment, to post-production monitoring.
You will work on high-impact domains across Masan's retail, FMCG, supply chain, and customer ecosystem, dealing with large-scale, multi-channel data.
Key Responsibilities
- Translate business problems into analytical/ML solutions with clear measurable impact.
- Perform EDA, build hypotheses, and validate insights using large-scale structured & unstructured data.
- Build, tune, and evaluate ML models (classification, forecasting, optimization, segmentation).
- Develop production-ready features and contribute to the Feature Store.
- Work on big data environments (Spark, PySpark, Databricks, Delta Lake).
- Ensure data quality, lineage, and consistency across pipelines.
- Package and deploy ML models using MLflow, FastAPI, Docker, and CI/CD workflows.
- Collaborate with Data/ML Engineers to operationalize models at scale.
- Set up monitoring dashboards: model performance, drift detection, data quality, retraining triggers.
- Continuously track model performance and business KPIs after deployment.
- Optimize and retrain models based on real-world feedback.
- Communicate findings, insights, and recommendations to business teams in a clear and actionable way.
- Explore new techniques (LLMs, embeddings, RAG) when relevant to business problems.
- Prototype solutions rapidly and iterate based on feedback.
Qualifications
For Middle-level candidates
- 2+ years of hands-on experience in applied machine learning.
- Strong Python skills (pandas, sklearn; PySpark is a plus).
- Solid understanding of statistics, algorithms, data structures.
- Experience building and validating production-ready ML models.
For Senior-level candidates
- 4+ years of end-to-end ML experience solving real business problems.
- Proven track record of deploying, monitoring, and optimizing ML models in production.
- Experience leading projects and partnering with cross-functional stakeholders.
Both levels
- Experience with MLflow, FastAPI, Docker, or similar deployment tools.
- Experience with cloud or big-data environments (Azure, Databricks, Spark/Delta).
- Strong communication and ability to explain technical topics to non-technical teams.
- Product-oriented mindset with strong ownership and a bias for impact.
Nice to have
- Hands-on with LangChain, OpenAI API, prompt engineering.
- Kaggle competitions, research publications, or AI side projects.
Key note: We understand that year-end can be a sensitive time for career transitions; therefore, we are open to negotiating the start date to best suit your situation.
