Advisor, Data Science

Dell

Singapore

On-site

SGD 90,000 - 120,000

Full time

14 days+

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Benefits offered by this job

Award-winning financial wellness tools

Generous leave of absence for new parents and caregivers

Job summary

Dell in Singapore is seeking an Advisor in Data Science to lead feature engineering and data discovery efforts in a dynamic team. You will collaborate with engineers and scientists to develop high-quality features for ML and GenAI applications while ensuring a comprehensive feature engineering lifecycle.

The position requires a strong background in data science, ML engineering, and robust software practices. Join us to leverage your skills in a role that promises both career growth and a chance to make a significant impact.

Qualifications

5–8 years of experience in ML engineering, data engineering, or data science.
Strong focus on feature engineering.
Experience with AI-assisted coding tools.

Responsibilities

Partner with data scientists and engineers on feature development.
Lead data discovery and feature identification.
Design and implement scalable feature pipelines.

Skills

Feature engineering

Data science

Machine learning engineering

Data analysis

Software engineering practices

Education

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field

Tools

SQL

Airflow

Feature stores (e.g., Feast, Tecton)

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

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

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

Build scalable and reusable feature pipelines using modern data processing frameworks

Ensure pipelines are:

Fault-tolerant and performant
Observable and testable

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:

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

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

Building production‑grade data pipelines and feature systems

Applying software engineering best practices to data/ML systems

Working with large‑scale structured and unstructured data

Feature stores (Feast, Tecton, or similar)
Vector databases and embedding pipelines
Graph databases and knowledge graphs
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.

Visit our Culture Code page to learn more about how we work and lead.