Scientist 4, Data Science

Responsibilities

• Lead the end-to-end design, architecture, and implementation of large-scale machine learning programs and LLM initiatives.
• Own the technical vision and roadmap for ML initiatives, ensuring alignment with business objectives.
• Define success metrics and KPIs for ML programs, establishing clear links between technical work and business outcomes.
• Mentor and guide junior and mid-level data scientists and ML engineers to accelerate technical growth.
• Build and maintain strong relationships with cross-functional partners, including product managers and executives.
• Drive innovation through rapid prototyping and staying at the forefront of ML/AI research.

Requirements

• PhD or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
• 10+ years of hands-on experience in machine learning or data science.
• 4+ years of experience leading technical projects or programs with demonstrated business impact.
• Expert-level proficiency in machine learning frameworks such as TensorFlow or PyTorch.
• Strong software engineering skills with proficiency in Python and production-grade code development.
• Proven experience with LLM agents, multi-agent systems, and deploying models to production at scale.
• Extensive experience with MLOps and big data technologies (e.g., Spark, Hadoop).

Preferred Qualifications

• Experience in specialized domains such as NLP, computer vision, or recommendation systems.
• Publications in top-tier AI conferences (e.g., NeurIPS, ICML, CVPR).
• Experience building and scaling ML/LLM platforms or infrastructure.
• Background in experimentation design and causal inference methodologies.
• Familiarity with ML model governance, fairness, and responsible AI practices.

About the Company

WD is building the infrastructure behind the AI-driven data economy. We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide.