Job Description

Strategically positioned within the organization of the CIO, AAO is missioned with accelerating Analytics solutions at scale across the Enterprise to rapidly capture business value. These solutions target key business metrics, such as, reducing manufacturing cost, improve capital efficiency, reduce time-to market to develop new products, improve planning, operational efficiency & logistics, and enhance customer experience.

AAO solutions are built using cutting edge Industrial 4.0 technologies and are delivered through a platform approach to enable rapid scaling. The solutions span AI/ML for improving manufacturing test, yield, quality & OEE, AI/ML & data analytics for sales, marketing, logistics & supply chain problems, Operations Research for capacity and scheduling optimization, Digital Twin for inventory and logistics optimization, and Product Telematics for managing customer fleet management solutions. Many of these solutions are first-of-a-kind that have discovery and rapid learning elements with the intent of taking solution to scale.

We are seeking a PhD Data Scientist to transform how we respond to market dynamics, optimize supply, cost, and pricing, and enhance our financial forecasting methods. In this role, you will collaborate with key stakeholders in Product Marketing, Sales, Finance, and Supply Chain to develop cutting-edge analytics solutions that drive significant impact on revenue, profitability, and shareholder value. With access to an exceptional talent pool in AAO, you will have the opportunity to fundamentally transform how Western Digital operates as a leader in the data storage industry.

ESSENTIAL DUTIES AND RESPONSIBILITIES 

• Probabilistic Demand Forecasting: Develop probabilistic models that provide a range of possible demand outcomes with associated probabilities. Update demand forecasts dynamically as new information becomes available, using Bayesian principles to incorporate uncertainty in a structured way.
• Demand vs. Supply Scenario Planning: Develop multiple scenarios based on different assumptions about future market conditions, demand trends, and external factors (e.g., macro-economic indicators, geo-political events).
• Simulations & Quantitative Risk Management: Use Monte Carlo simulations and other probabilistic models to quantify & minimize the risk of different demand scenarios on inventory levels, production schedules, and profitability.
• Financial Forecasting: Utilize financial econometrics to build models that support financial forecasting, including sales and revenue projections.
• Pricing Optimization: Apply advanced analytics and optimization techniques to pricing strategies, including price gap analysis and pricing optimization.
• Data Analysis: Acquire, cleanse, and transform large and complex datasets and analyze to extract actionable insights and support scenario planning & risk assessment models.
• Collaboration with Cross-Functional Teams: Work closely with Product Marketing, Supply Chain, Sales, and Finance teams to identify business challenges and develop data-driven solutions.
• Continuous Learning & Innovation: Stay up-to-date with the latest trends and technologies in econometrics, advanced statistics, game theory, and data science. Contribute to the continuous improvement of processes, tools, and methodologies.

Qualifications

REQUIRED

• PhD in a field such as Economics & Data Science, Advanced Statistics, Applied Mathematics, or a related discipline. Recent graduates and post-doc students are invited to apply.
• Research experience in solving complex forecasting, risk management and cost or profit optimization problems.
• Deep expertise in advanced statistical methods (e.g., Causal inference and Bayesian statistics), mathematical optimization, scenario planning, and simulations (e.g., Monte Carlo Simulations).
• Ability to handle large and complex datasets, with proficiency in SQL, Hadoop, Spark, and NoSQL databases.
• Proficiency in programming languages such as Python, R, or similar for data analysis and model development.
• Prior experience or aptitude to learn Machine Learning, Deep Learning, and Generative AI techniques.
• Ability to quickly learn and apply new tools and technologies, particularly in data visualization (e.g., Power BI, Spotfire) and cloud platforms (e.g., Oracle Cloud, AWS, GCP).

SKILLS:

• Strong interpersonal and communication skills with the ability to work effectively with cross-functional teams and present complex data-driven insights to non-technical stakeholders.
• Critical thinking and an appetite to solve complex real-world problems.