Data Scientist – Enterprise AI Solutions
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Data Scientist – Enterprise AI Solutions
Location
Petaling Jaya, Malaysia
Experience
Mid
Posted
Jul 10, 2026
Apply by
August 9, 2026
Applicants
0
Early applicantFull-timeWork from Office
Job Description
**Make an impact with NTT DATA**
Join a company that is pushing the boundaries of what is possible. We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society. Our workplace embraces diversity and inclusion – it’s a place where you can grow, belong and thrive.
**Your day at NTT DATA**
The **Data Scientist** will design, develop, and deploy machine learning and AI solutions that solve real business challenges across banking, healthcare, and other industries.
You will work across the full AI lifecycle, from data exploration and feature engineering to predictive modelling, machine learning, Generative AI, and MLOps. Working closely with Software Engineers, Technical Leads, and Solution Architects, you will help transform data into intelligent solutions that deliver measurable business value.
**Responsibilities**
- Design, develop, train, and evaluate machine learning models for predictive analytics and business intelligence.
- Build and optimise classification, regression, clustering, recommendation, forecasting, anomaly detection, and other predictive models.
- Analyse structured and unstructured datasets to uncover business insights and opportunities.
- Prepare, clean, transform, and engineer datasets for machine learning applications.
- Develop, validate, and monitor machine learning models throughout their lifecycle.
- Build and optimise Retrieval-Augmented Generation (RAG) pipelines and Generative AI solutions where applicable.
- Evaluate Large Language Models (LLMs) and improve AI solution performance through prompt engineering, retrieval optimisation, and model evaluation.
- Develop AI and machine learning pipelines using enterprise AI platforms.
- Work closely with Software Engineers to deploy AI models into production applications.
- Research emerging AI and machine learning technologies and recommend practical solutions for customer use cases.
- Document methodologies, experiments, and technical findings to support knowledge sharing and continuous improvement.
**Required Qualifications & Experience**
- Bachelor's Degree in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline.
- Minimum **3 years** of experience in Data Science, Machine Learning, or Artificial Intelligence.
- Strong proficiency in Python for data science and machine learning.
- Solid understanding of supervised and unsupervised machine learning algorithms.
- Experience building predictive models using real-world datasets.
- Experience with feature engineering, model training, validation, and optimisation.
- Strong knowledge of statistics, probability, and data analysis techniques.
- Experience with SQL and data querying.
- Experience using Git and collaborative development workflows.
- Strong analytical thinking and problem-solving skills.
- Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders.
# **Preferred Qualifications**
Experience in one or more of the following areas is highly desirable.
### **Machine Learning & Data Science**
- Predictive Analytics
- Classification, Regression, Clustering, Forecasting, Recommendation Systems
- Time Series Analysis
- Feature Engineering
- Model Evaluation and Optimisation
- Statistical Modelling
- Natural Language Processing (NLP)
- Computer Vision
- Explainable AI (XAI)
### **AI & Generative AI**
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- AI Agents and Agentic Workflows
- Prompt Engineering
- AI Evaluation Frameworks
- Embeddings and Semantic Search
- Vector Databases
### **AI & Data Platforms**
- Databricks
- Dataiku
- Snowflake
- Microsoft Fabric
- Azure Machine Learning
- AWS SageMaker
- MLflow
- MLOps pipelines
- Model deployment and monitoring
### **Programming & Cloud**
- Scikit-learn
- TensorFlow or PyTorch
- Pandas and NumPy
- Docker
- Azure or AWS
- CI/CD for machine learning solutions
**Workplace type:**
**About NTT DATA**
NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune
Global 100. We are committed to accelerating client success and positively impacting society through
responsible innovation. We are one of the world’s leading AI and digital infrastructure providers, with
unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and
application services. Our consulting and industry solutions help organizations and society move
confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more
than 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as
established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each
year in R&D.
**Equal Opportunity Employer**
NTT DATA is proud to be an Equal Opportunity Employer with a global culture that embraces diversity. We are committed to providing an environment free of unfair discrimination and harassment. We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status, or any other protected category. Join our growing global team and accelerate your career with us. Apply today.
**Third parties fraudulently posing as NTT DATA recruiters**
NTT DATA recruiters will never ask job seekers or candidates for payment or banking information during the recruitment process, for any reason. Please remain vigilant of third parties who may attempt to impersonate NTT DATA recruiters whether in writing or by phone in order to deceptively obtain personal data or money from you. All email communications from an NTT DATA recruiter will come from an **@nttdata.com** email address. If you suspect any fraudulent activity, please [*contact us*](mailto:global.careers@nttdata.com).
Key Responsibilities
- Design, develop, train, and evaluate machine learning models for predictive analytics and business intelligence.
- Build and optimize classification, regression, clustering, recommendation, forecasting, and anomaly detection models.
- Analyze structured and unstructured datasets to uncover business insights and opportunities.
- Prepare, clean, transform, and engineer datasets for machine learning applications.
- Develop, validate, and monitor machine learning models throughout their lifecycle.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines and Generative AI solutions.
- Evaluate Large Language Models (LLMs) and improve AI solution performance through prompt engineering and retrieval optimization.
- Develop AI and machine learning pipelines using enterprise AI platforms.
- Collaborate with Software Engineers to deploy AI models into production applications.
- Research emerging AI and machine learning technologies and recommend practical solutions for customer use cases.
- Document methodologies, experiments, and technical findings to support knowledge sharing and continuous improvement.
Requirements
- Bachelor's Degree in Data Science
- Computer Science
- Artificial Intelligence
- Statistics
- Mathematics
- Engineering
- or a related discipline
Skills Required
PythonSupervised Machine LearningUnsupervised Machine LearningFeature EngineeringModel TrainingModel ValidationModel OptimizationStatisticsProbabilityData AnalysisSQLGitCollaborative Development WorkflowsAnalytical ThinkingProblem SolvingCommunicationPredictive AnalyticsClassificationRegressionClusteringForecastingRecommendation SystemsTime Series AnalysisStatistical ModellingNatural Language Processing (NLP)Computer VisionExplainable AI (XAI)Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)AI AgentsAgentic WorkflowsPrompt EngineeringAI Evaluation FrameworksEmbeddingsSemantic SearchVector DatabasesDatabricksDataikuSnowflakeMicrosoft FabricAzure Machine LearningAWS SageMakerMLflowMLOps pipelinesModel deploymentModel monitoringScikit-learnTensorFlowPyTorchPandasNumPyDockerCI/CD
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