Advisor, Data Science

Advisor, Data Science (Feature engineer) - Global Ops Data Science

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.

Join us to do the best work of your career and make a profound social impact as an Advisor, data science Team in Singapore.

You will…

• Partner closely with data scientists, ML engineers, and domain experts to design and deliver high-quality features that power ML and GenAI systems
• Lead data discovery and feature identification efforts across complex structured and unstructured datasets
• Own the end-to-end feature engineering lifecycle, including ingestion, transformation, validation, and productionization
• Design and implement robust, scalable feature pipelines and services using strong software engineering principles
• Bring a software engineering (ML engineering) mindset to data and feature development, ensuring reliability, performance, and maintainability
• Leverage AI-assisted coding tools (e.g., Copilot, LLM-based tools) while maintaining high standards of code review, correctness, and efficiency
• Drive innovation in feature engineering, including embeddings, representation learning, and data-centric AI approaches
• Work with ML engineers to integrate features into training, inference, and real-time decision systems
• Mentor junior team members and help establish best practices in feature development and data engineering

Essential Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field with 5–8 years of experience in ML engineering, data engineering, or data science, with a strong focus on feature engineering

• Feature Engineering & Data Discovery (Core Focus)

• Lead feature identification and engineering across:

  • Structured data (SQL, data warehouses, relational systems)
  • Unstructured data (text, logs, documents, semi-structured sources)

• Perform deep exploratory data analysis (EDA) to uncover patterns, anomalies, and predictive signals

• Apply advanced techniques:

  • Feature extraction, transformation, and scaling
  • Embeddings and representation learning
  • Feature selection and dimensionality reduction

• ML Engineering & Software Engineering Excellence

• Strong foundation in software engineering practices, including:

  • Writing production-quality, modular, testable code
  • API and service development (for feature serving)
  • Version control, CI/CD, and system reliability

• Design and implement feature pipelines as scalable systems, not just scripts

• Build and maintain data/feature services for both batch and real-time use cases

• Collaborate on model training and inference pipelines, ensuring seamless integration

• Unstructured Data & GenAI Feature Development

• Develop features for NLP and GenAI applications, including:

  • Text preprocessing, tokenization, and normalization
  • Embedding generation and similarity search features

• Support and enhance RAG pipelines and LLM-based workflows with high-quality data representations

• Contribute to agentic systems, especially around context construction, state, and data grounding

• Data Engineering & Feature Pipelines

• Build scalable and reusable feature pipelines using modern data processing frameworks

• Ensure pipelines are:

  • Fault-tolerant and performant
  • Observable and testable

• Implement effi