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Advisor, Data Science

Dell Technologies Capital
5-8 Years
Singapore
  • Posted 6 hours ago
  • Be among the first 10 applicants

Job Description



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 efficient data transformations for large-scale datasets

          • In-depth hands-on experience in Enterprise Database Management

          • Experience with Airflow data pipelines for orchestrating and scheduling feature and data workflows


          • Coding Assist & Code Quality



            • Use AI-assisted coding tools to enhance productivity

            • Critically review and validate tool-generated code, ensuring correctness, efficiency, and security

            • Maintain high standards of code quality, testing, and documentation


          • Data Quality, Validation & Monitoring



            • Implement robust data validation and feature quality checks

            • Monitor:

              • Data consistency
              • Feature drift
              • Pipeline health

            • Ensure traceability and reproducibility of features used across models


            • Collaboration & Technical Leadership



              • Act as a bridge between data science and ML engineering, aligning feature design with modeling needs

              • Provide technical leadership on feature engineering best practices

              • Mentor I5/I6 team members and contribute to design and code reviews


            • Innovation & Applied Research



              • Drive innovation in:

                • Feature engineering frameworks and tooling
                • Data-centric AI and representation learning

              • Experiment with and adopt emerging approaches in GenAI, embeddings, and feature stores

              • Lead or contribute to prototyping and innovation initiatives

                • Desirable Requirements:


              • Proven experience:



                • Building production-grade data pipelines and feature systems

                • Applying software engineering best practices to data/ML systems

                • Working with large-scale structured and unstructured date


              • Experience with:


                • Feature stores (Feast, Tecton, or similar)

                • Vector databases and embedding pipelines

                • Graph databases and knowledge graphs

                • Enterprise database management systems

                • Airflow or similar workflow orchestration tools


              • Understanding of:


                • Agentic memory architectures (short-term, long-term, contextual memory)

                • Combining vector, graph, and memory-based approaches for richer AI systems


              • Familiarity with:


                • MLOps / LLMOps practices

                • Real-time feature serving architectures


              • Experience in supply chain or domain-specific analytics


                • Who we are


                We believe that each of us has the power to make an impact. That's why we put our team members at the center of everything we do. If you're looking for an opportunity to grow your career with some of the best minds and most advanced tech in the industry, we're looking for you.

                Dell Technologies is a unique family of businesses that helps individuals and organizations transform how they work, live and play. Join us to build a future that works for everyone because Progress Takes All of Us.

                Dell Technologies is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. Read the full Equal Employment Opportunity Policy here.



    More Info

    Job Type:
    Permanent Job
    Industry:
    Other
    Function:
    Data Science
    Employment Type:
    Full time

    Job ID: 152546333

    Last Updated: 20-08-2026 10:29:23 PM
    Homejobs in SingaporeAdvisor, Data Science

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