Senior Analyst, Data Science

DELL GLOBAL B.V. (Singapore Branch); Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

Dell Technologies is seeking a Senior Analyst, Data Science to join its Supply Chain Data Science team in Singapore. You will translate business problems into data-driven solutions, build predictive and prescriptive models, and work with data scientists, engineers, and domain experts to deliver end-to-end analytics products.

You will apply GenAI techniques, develop prompt pipelines and retrieval systems, and contribute to AI initiatives while ensuring code quality, scalability, and production

Qualifications

• 2–4 years of data science/ML analytics experience with relevant degree.
• Experience with large datasets in production environments.
• Applied data science and solution delivery from data prep to evaluation.
• GenAI techniques: prompt engineering, retrieval-augmented generation, basic LLM workflows.
• Experience with coding-assist tools and maintaining clean code.

Responsibilities

• Translate business problems into data-driven solutions with data scientists and engineers.
• Deliver end-to-end analytics products from data exploration to deployment.
• Work with cross-functional teams to integrate models into production systems.
• Contribute to GenAI components, prompt pipelines, and evaluation workflows.
• Stay current with emerging ML/GenAI techniques and apply to business problems.

Skills

LLM tools

RAG pipelines

Large datasets

Python

Code quality

Experimentation

Data integration

AI workflows

Prompt engineering

Model deployment

Education

Bachelor’s or Master’s degree in Statistics/CS/Engineering/Mathematics

Tools

Azure OpenAI

LLM-based workflows

APIs/containers/cloud platforms

Job description

Senior Analyst, Data Science (Applied AI, GenAI & Advanced Analytics)

Dell Technologies is a leader in providing technology infrastructure to its customers in an era increasingly being driven by digital and data. Enabling Dell to satisfy its customers’ needs hinges on executing a world class supply chain, connecting together sales orders with a complex ecosystem of partners and suppliers. Data plays an integral role in this as we digitize and modernize our supply chain. Join our Data science team within Supply chain as a data scientist to solve our most challenging business problems with statistical, predictive and prescriptive approaches, making our decision making faster and more sophisticated. We offer a competitive remuneration package.

What you’ll achieve:

As a Senior Analyst, you will work with data scientists, engineers, and supply chain domain experts to translate business problems into data-driven solutions.

Join us to do the best work of your career and make a profound social impact as a Senior Analyst, Data Science Team in Singapore.

You will also:

• Work with data scientists, engineers, and supply chain domain experts to translate business problems into data-driven solutions
• Deliver end-to-end solutions for moderately complex problems, from data exploration to model deployment, with support from senior team members.
• Use AI-assisted coding tools (e.g., Copilot, LLM-based tools) to improve productivity, while ensuring correctness and maintainability of generated code
• Contribute to GenAI and agentic solutions, including building components such as prompt pipelines, retrieval systems, and evaluation workflows
• Participate in experimentation and innovation initiatives, such as prototyping new approaches and applying emerging AI techniques to business problems
• Collaborate with cross-functional teams to integrate models into production systems
• Share learnings with peers and contribute to a data science community of practice
• Continuously grow technical skills through a structured development plan

Essential Requirements

1. 2 to 4 years of experience (or equivalent) in data science, ML, or analytics with a Bachelor’s or Master’s degree in Statistics, Computer Science, Engineering, Mathematics and experienced in:

• LLM tools or platforms (e.g., Azure OpenAI or similar)
• Basic RAG pipelines or embeddings
• Working with large datasets in production environments

2. Applied Data Science & Solution Delivery

• Develop, evaluate, and deploy machine learning and statistical models to solve business problems
• Own well-defined problem areas end-to-end, including data preparation, modeling, and performance evaluation

3. GenAI & Emerging AI Techniques

• Hands-on implementation of GenAI components and workflows, including:
• Prompt engineering
• Retrieval-augmented generation (RAG)
• Basic LLM-based workflows
• Assist in developing agentic or multi-step AI workflows under guidance
• Evaluate outputs for quality, relevance, and reliability

4. Coding Assist & Code Quality

• Use coding-assist tools effectively to accelerate development
• Review, debug, and maintain tool-generated code, ensuring quality and correctness
• Write clean, well-documented, and testable code following software engineering best practices

5. Modeling & Analytics

• Build supervised and unsupervised models including regression, classification, clustering, forecasting, and basic NLP
• Perform exploratory data analysis and feature engineering on structured and unstructured datasets
• Design and execute experiments (e.g., hypothesis testing, experimentation frameworks), select and tune models to optimize performance.

6. Data & Systems Integration

• Query and process data from SQL and unstructured sources
• Work with engineering teams to deploy models into production environments
• Own model deployment with support from engineering or senior team members

7. Programming & Tools

• Strong proficiency in Python
• Familiarity with common data science libraries and workflows
• Awareness of scalability and performance considerations

8. Innovation & Research

• Contribute to innovation through experimentation, prototyping, and applying new techniques
• Stay current with emerging trends in ML and GenAI, and apply them where relevant
• Participate in team-level research or hackathon initiatives

Desirable Requirements

• Exposure to model deployment (APIs, containers, or cloud platforms) and cross-functional collaboration in delivering data products
• Familiarity with MLOps / LLMOps concepts. Experience in supply chain, logistics, or operations analytics. Familiarity with MLOps / LLMOps concepts. Participation in innovation initiatives, hackathons, or applied research projects