Senior Analyst, Data Science

Dell Technologies; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Dell Technologies in Singapore is seeking a Senior Analyst, Data Science to translate business problems into data-driven solutions within the Supply Chain Data Science team. The role involves building end-to-end ML models, GenAI components, and AI-powered workflows, collaborating with data scientists, engineers, and domain experts to productionize models.

You will work on prompt engineering, RAG pipelines, and scalable Python-based data pipelines, contributing to a robust data culture and

Qualifications

• 2-4 years of experience in data science, ML, or analytics with a relevant degree.
• Experience with LLM tools or platforms (e.g., Azure OpenAI).
• Working with large datasets in production environments.
• Desirable exposure to model deployment and cross-functional collaboration.

Responsibilities

• Translate business problems into data-driven solutions with cross-functional teams.
• Deliver end-to-end analytics solutions from data exploration to deployment.
• Contribute to GenAI components, prompt pipelines, and evaluation workflows.
• Collaborate to integrate models into production systems.
• Share learnings with Data Science community and grow technical skills.

Skills

LLM tools

RAG pipelines

Large datasets

Python

ML modeling

Prompt engineering

GenAI workflows

Code quality

Education

Bachelor’s or Master’s in Statistics/CS/Engineering/Math

Tools

Azure OpenAI

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 aSenior Analyst, Data Science TeaminSingapore.

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