Senior Analyst, Data Science

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