Scientist 5, Data Science

Role summary
We’re hiring a hands-on technologist to lead the design, delivery and operational scaling of mission-critical AI/ML systems. This is a senior technical leadership role, combining end-to-end system design, deep ML engineering, research translation and team enablement. You will set technical direction, unblock engineering teams, and take direct ownership for measurable business outcomes.

Core responsibilities

• Own end-to-end system design and architecture for production ML/AI solutions — from problem framing, data design, model selection and infra to monitoring, runbooks and cost control.
• Lead hands-on technical delivery: prototype, validate, harden and ship models and agentic components into live systems; ensure reliability, observability, and automated CI/CD for models and data pipelines.
• Act as the single technical escalation point — remove blockers, resolve cross-team technical tradeoffs, and make final architecture decisions that balance performance, scalability, cost and vendor dependencies.
• Mentor and grow engineering teams (ML engineers, data engineers, OR engineers), set engineering standards, code/architecture reviews and champion best practices (MLOps, testing, data contracts).
• Translate research into product: evaluate papers, run experiments, lead IP efforts (patents, trade secrets) and supervise research to production pipelines.
• Drive multiple projects in parallel with clear prioritization, milestones and delivery SLAs; align technical plans to business KPIs.
• Define, track and report success metrics and ROI for all ML initiatives; continuously tune models and design experiments for measurable impact.
• Collaborate with product, platform, security, legal and operations teams to ensure compliance, data privacy and safe, explainable model behavior.
• Works closely with Product, Platform, Security, Legal, and Business stakeholders.
• Become a visible technical leader within the company — represent technical strategy externally when needed.

Must-have qualifications & experience

• Demonstrated  experience in Machine Learning/AI engineering and solution delivery, across classical ML, generative models and agentic/LLM-based systems.
• Proven ability to design production ML platforms (data ingestion → training → serving → monitoring → retraining) with scalability, reliability and cost awareness.
• Deep expertise in system & distributed design: data architectures, feature stores, model serving, streaming/batch pipelines, autoscaling, retries/poison-pill handling and disaster recovery.
• Strong MLOps and DevOps experience: CI/CD for models, monitoring (data + model drift), A/B testing, canary deployment and rollback strategies.
• Strong practical experience in classical ML, deep learning and modern LLM/agentic systems (RAG, fine-tuning, evaluation, guardrails) using Python and modern ML frameworks (PyTorch/TensorFlow).
• Experience with CI/CD for models, containerization (Docker/Kubernetes), model serving, monitoring, drift detection and automated retraining pipelines.
• Strong coding and service design (APIs/microservices), testing practices, high-availability design, observability and incident handling for live AI systems.
• Experience mentoring/leading senior engineers and small cross-functional teams; comfortable as the technical owner across several concurrent initiatives.
• Prior experience publishing research and participating in IP creation (patent filings, trade secrets) is required.
• Excellent communication skills — able to present technical tradeoffs to both engineering and executive stakeholders.

Preferred skills

• Background with reinforcement learning, foundation models, LLMs and agent orchestration frameworks.
• Hands-on with cloud platforms (AWS/GCP) and on-prem hybrid deployments.
• Strong software engineering fundamentals: scalable microservices, API design, security best practices, and cost optimization.
• Familiarity with optimization/OR techniques and integrating them with ML pipelines.

System-design expectations

• Lead architecture reviews and design sessions; produce clear system diagrams, component ownership, latency/capacity budgets, cost estimations and failure-mode analyses.
• Define data contracts, SLAs, service-level objectives and monitoring thresholds for every deliverable.
• Ensure designs are modular, testable and observable — with clear automation for deployment, rollback and incident response.
• Make pragmatic architecture choices: prefer simpler solutions that meet business needs and constrain cost/dependencies; justify when heavy engineering is necessary.

Because Western Digital thrives on the power of diversity and is committed to an inclusive environment where every individual can thrive through a sense of belonging, respect, and contribution, we are committed to giving every qualified applicant and employee an equal opportunity.  Western Digital does not discriminate against any applicant or employee based on their protected class status and complies with all federal and state laws against discrimination, harassment, and retaliation, as well as the laws and regulations set forth in the "Equal Employment Opportunity is the Law" poster.

WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.

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