Staff Applied Data Scientist - Causal AI for Demand Forecasting

at  Cisco Systems

Who we are: The post-pandemic years have exposed inherent biases and limitations in expert-driven and statistical/Traditional ML-based forecasting approaches. Cisco wasn’t immune and saw a 4X increase in backlog, revenue impact, and a subsequent 3X inventory increase. The Forecasting Data Science team within Global Planning is solving this by pioneering the application of Causal AI to re-invent Demand Forecasting of Cisco’s product portfolio to provide breakthrough levels of regime-resilient forecast accuracy, efficiency, and prescriptive insights that enable game-changing opportunities across Cisco and its Supply Chain. The team was recognized by Gartner in their Power of Profession 2024 Supply Chain awards as one of the top 5 in the world in the Process and Technology Innovation category.
Who you will work with:
A high caliber and engaged team plus an eco-system of world-leading AI partners chartered with developing and operationalizing an inspectable, multi-dimensional system of causal models that provides an integrated, comprehensive, and evidence-based point-of-view of Cisco’s short and long-term demand at aggregated and product levels.
This team is responsible for incorporating planning, product, sales, and customer intelligence from across the enterprise and from external global macro-economic and market data that relates to the demand for Cisco’s products into the structure of this system of models. The team delivers and continuously improves AI-based forecasts, forecast ranges, and financial and prescriptive insights from this system through connections with Planning and other Supply Chain and Enterprise teams for the different facets of Cisco’s business.
The difference you will make:
You will provide the technical and architectural vision and leadership to a core team and bring your expertise, experience, and innovation to solve the challenges that hinder developing and implementing an industry-leading Causal AI-based forecasting system that effectively enhances decision rigor and maximizes operational efficiencies across Enterprise and Supply Chain functions.

What you will do:

• Develop the Causal-AI based Forecasting system for Aggregated Demand.
• Improve the efficiency and scalability of the Forecasting System.
• Provide integrated, reconciled, logically sound evidence-based views for different facets of Cisco’s short and long-term demand.
• Develop reliable approaches for uncertainty quantification to enable scenario/range forecasts.
• Leverage and incorporate appropriate machine learning approaches including customization of recently published research as needed to build better Causal AI solutions.
• Connect with stakeholders to communicate the short-and-long term AI forecasts and the changes in these forecasts. Discern and articulate the story in the forecasts and forecast changes, areas of discrepancies or differences with expert forecasts, understanding and accounting for the confidence level of these forecasts.
• Continuously improve this system to improve forecast accuracy and incorporate learnings from formal and informal collaborations with stakeholders and other experts into the AI system.
• Provide technical direction and leadership to the core team of Data Scientists, Data Engineers, and AI partners to help develop a world-leading effort.

Minimum Qualifications:

• Extensive Advanced Analytics experience or demonstrable experience with a PhD in Statistics, Mathematics or Applied Mathematics, Physics, Engineering, or related quantitative field.
• Strong all-round foundation in AI and machine learning, with a theoretical and practical understanding of Causal machine learning approaches.
• Mastery in Python, with advanced data analysis and data engineering skills, including using SQL at scale
• Sharp eye for patterns and numbers and able to draw out the story and conclusions from data and modeling experiments in real-time.
• Deep coding experience in developing and operationalizing scalable ML solutions in cloud environments.
• Demonstrated structured learning and problem solving skills using AI/ML, including in hackathon-like settings and in working with a geographically dispersed team to solve challenging problems.
• Demonstrated mentoring and coaching success, fostering team growth and knowledge sharing.
• Exceptional communication skills with an ability to unpack complex problems, and articulate AI/ML approaches, solutions, and results for executives.

Preferred Qualifications:

• Expert-level practical understanding of global financial markets, macro-economics, and micro-economics. Proven ability to leverage this understanding in developing better models and articulating the story of the modelling results.
• Experience using Causal AI and Structured Causal Models in Demand Forecasting and ideally also in other complex or dynamic domains.
• Expert understanding of statistics and causal inference.
• Project management skills, with an ability to deliver results in a fast-paced environment.
• A customer-centric approach to problem-solving and a knack for envisioning, translating business requirements into analytics requirements, and realizing feasible data science solutions.
• Drive to learn, innovate, and make a meaningful impact, with a strong desire to influence and shape the direction and application of Causal AI in the Enterprise.
• A pioneering spirit, eager to join at the beginning stages of a new venture within a well-established company.
• A strong bias for action, delivering iterative results quickly rather than waiting for perfection.
• Ability to directly manage personal and team-level stakeholder priorities.
• Comfortable and capable of interacting independently with technologists and business experts in other domains, as with business executives.

• Develop the Causal-AI based Forecasting system for Aggregated Demand.
• Improve the efficiency and scalability of the Forecasting System.
• Provide integrated, reconciled, logically sound evidence-based views for different facets of Cisco’s short and long-term demand.
• Develop reliable approaches for uncertainty quantification to enable scenario/range forecasts.
• Leverage and incorporate appropriate machine learning approaches including customization of recently published research as needed to build better Causal AI solutions.
• Connect with stakeholders to communicate the short-and-long term AI forecasts and the changes in these forecasts. Discern and articulate the story in the forecasts and forecast changes, areas of discrepancies or differences with expert forecasts, understanding and accounting for the confidence level of these forecasts.
• Continuously improve this system to improve forecast accuracy and incorporate learnings from formal and informal collaborations with stakeholders and other experts into the AI system.
• Provide technical direction and leadership to the core team of Data Scientists, Data Engineers, and AI partners to help develop a world-leading effort