Data Scientist Jobs in Singapore | AI & ML Careers
Job Overview
Explore Data Scientist Jobs in Singapore entry to senior level roles for professionals interested in building data-driven products, predictive models, Artificial Intelligence solutions, and advanced analytics platforms. These opportunities are suitable for Data Scientists across entry, mid, senior, and lead career levels with expertise in Python, SQL, Machine Learning, Generative AI, Deep Learning, Statistical Modeling, NLP, Data Analytics, Cloud Computing, and MLOps.
More About Data Scientist Job role
As a Data Scientist, you will transform complex business and operational data into actionable insights and production-ready analytical solutions. The role involves data exploration, feature engineering, predictive modeling, experimentation, model evaluation, visualization, and collaboration with engineering and business teams.
Experienced professionals may also work on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), recommendation systems, forecasting, fraud detection, customer analytics, AI automation, and enterprise machine learning platforms.
Candidates with strong analytical foundations and practical experience with modern data science technologies are encouraged to explore these Data Scientist career opportunities across Singapore.
Job Title
Position: Data Scientist / Senior Data Scientist / AI Data Scientist / Machine Learning Data Scientist
Location: Singapore
Experience Level: Entry Level to Senior / Lead Roles
Employment Type: Full-Time / Permanent / Contract Opportunities
Work Model: On-site / Hybrid / Remote, depending on the opportunity
Job Function: Data Science, Artificial Intelligence, Machine Learning & Advanced Analytics
Career Level: Entry, Associate, Mid-Level, Senior & Lead
Roles & Responsibilities
- Collect, clean, transform, and analyze large structured and unstructured datasets from multiple business and technology sources.
- Build scalable Machine Learning and Artificial Intelligence models to solve complex business problems.
- Perform exploratory data analysis (EDA) to identify patterns, trends, anomalies, correlations, and actionable business insights.
- Develop predictive models for classification, regression, forecasting, recommendation, segmentation, anomaly detection, and optimization.
- Apply statistical techniques including hypothesis testing, probability analysis, regression, experimentation, and statistical inference.
- Perform feature engineering, feature selection, model training, validation, tuning, and performance optimization.
- Develop solutions using Python, SQL, Pandas, NumPy, Scikit-learn, XGBoost, PyTorch, and TensorFlow.
- Work with large datasets using technologies such as Apache Spark, Databricks, and distributed data processing platforms.
- Build NLP solutions for text classification, sentiment analysis, information extraction, semantic search, summarization, and document intelligence.
- Develop and evaluate deep learning models using neural networks and Transformer architectures.
- Work with Generative AI and Large Language Models (LLMs) to develop intelligent data and knowledge applications.
- Build RAG-based applications by combining enterprise data with LLMs, embeddings, semantic search, and vector databases.
- Develop recommendation engines, customer intelligence systems, demand forecasting models, risk models, and other data-driven applications.
- Design and execute A/B tests and experiments to evaluate products, models, and business strategies.
- Develop dashboards and visualizations using Power BI, Tableau, Matplotlib, Plotly, or similar technologies.
- Deploy machine learning models through APIs, batch pipelines, cloud services, or containerized environments.
- Work with AWS, Microsoft Azure, or Google Cloud Platform for scalable data processing, machine learning, and AI workloads.
- Implement MLOps practices for model versioning, deployment, monitoring, retraining, governance, and lifecycle management.
- Monitor model performance, data drift, prediction quality, and production reliability.
- Communicate analytical findings and model results to technical and non-technical stakeholders.
- Collaborate with Data Engineers, ML Engineers, AI Engineers, Software Engineers, Product Managers, and business teams.
- Senior Data Scientists may lead solution architecture, experimentation strategy, model governance, technical mentoring, and enterprise AI initiatives.
Technical Skill Set
| Skill Area | Technologies / Competencies |
|---|---|
| Programming | Python, SQL, R, Scala |
| Data Analysis | Pandas, NumPy, SciPy |
| Machine Learning | Scikit-learn, XGBoost, LightGBM, CatBoost |
| Deep Learning | PyTorch, TensorFlow, Keras |
| Generative AI | LLMs, Prompt Engineering, RAG, AI Agents |
| LLM Frameworks | LangChain, LlamaIndex, Hugging Face |
| NLP | Transformers, BERT, Text Classification, NER, Semantic Search |
| Statistics | Probability, Regression, Hypothesis Testing, Statistical Inference |
| Predictive Analytics | Classification, Regression, Forecasting, Clustering |
| Experimentation | A/B Testing, Experimental Design, Causal Analysis |
| Big Data | Apache Spark, PySpark, Databricks |
| Databases | PostgreSQL, MySQL, MongoDB, NoSQL |
| Data Warehousing | Snowflake, BigQuery, Amazon Redshift |
| Cloud | AWS, Microsoft Azure, Google Cloud Platform |
| Cloud ML | Amazon SageMaker, Azure Machine Learning, Vertex AI |
| MLOps | MLflow, Kubeflow, Model Monitoring, Model Registry |
| Containers | Docker, Kubernetes |
| Visualization | Tableau, Power BI, Matplotlib, Plotly |
| APIs | FastAPI, Flask, REST APIs |
| Vector Search | Pinecone, FAISS, Weaviate, Chroma, pgvector |
Required Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative discipline.
- Strong programming proficiency in Python and practical knowledge of SQL.
- Solid understanding of statistics, probability, linear algebra, algorithms, and machine learning fundamentals.
- Hands-on experience with data manipulation libraries such as Pandas and NumPy.
- Knowledge of machine learning techniques including regression, classification, clustering, forecasting, and model evaluation.
- Experience with Scikit-learn or comparable machine learning frameworks.
- Ability to clean, prepare, analyze, and model complex datasets.
- Understanding of feature engineering, cross-validation, hyperparameter optimization, and performance metrics.
- Ability to communicate analytical findings through clear visualizations and business-focused explanations.
- Familiarity with relational databases, data pipelines, and modern data architectures.
- Understanding of software engineering practices, Git, APIs, and reproducible data science workflows.
- Entry-level candidates should demonstrate relevant internships, academic projects, research, certifications, or practical Data Science projects.
- Experienced candidates should demonstrate the ability to deliver machine learning or analytical solutions addressing real-world business requirements.
Preferred Qualifications
- Advanced degree in Data Science, Computer Science, Statistics, Mathematics, AI, or a related quantitative discipline.
- Hands-on experience with Generative AI, LLMs, RAG, embeddings, prompt engineering, or AI agents.
- Experience with PyTorch, TensorFlow, Hugging Face, LangChain, or LlamaIndex.
- Knowledge of deep learning and Transformer architectures.
- Experience with big-data platforms such as Apache Spark, PySpark, or Databricks.
- Hands-on experience with Snowflake, BigQuery, Amazon Redshift, or modern cloud data warehouses.
- Experience deploying ML solutions on AWS, Azure, or Google Cloud Platform.
- Knowledge of Amazon SageMaker, Azure Machine Learning, or Google Vertex AI.
- Experience implementing MLOps and model lifecycle management.
- Familiarity with Docker, Kubernetes, MLflow, and CI/CD for machine learning.
- Experience with recommendation systems, forecasting, anomaly detection, fraud analytics, customer analytics, or risk modeling.
- Understanding of responsible AI, explainable AI, model governance, data privacy, and AI security.
- Experience translating complex analytical findings into measurable business outcomes.
Desired Soft Skills
- Strong analytical and quantitative problem-solving ability.
- Ability to approach ambiguous business challenges using structured data-driven methodologies.
- Strong attention to data quality and model accuracy.
- Excellent verbal, written, and presentation skills.
- Ability to explain complex statistical and machine learning concepts to non-technical stakeholders.
- Strong collaboration skills across data, engineering, product, and business teams.
- Curiosity and willingness to experiment with emerging AI and Data Science technologies.
- Ability to balance technical sophistication with practical business requirements.
- Strong ownership and accountability for analytical solutions.
- Ability to prioritize multiple projects and work effectively in dynamic environments.
- Critical thinking and evidence-based decision-making skills.
- Leadership, mentoring, and stakeholder-management capabilities for senior and lead positions.
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