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Applied Data Scientist
3 months ago
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 groundbreaking opportunities across Cisco and its Supply Chain. Gartner recognized the team 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 macroeconomic 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 bring your expertise, experience, and innovation to play a significant role in solving the challenges that hinder the development and implementing of an industry-leading Causal AI-based forecasting system that enhances decision rigor and improves operational performance and efficiencies across Cisco’s Enterprise and Supply Chain functions.
WHAT YOU WILL DO:
- Develop, evolve, and sustain key elements of the Causal-AI based Forecasting system for Aggregated Demand.
- Develop high-quality, accurate, economical models that are robust and have a long shelf-life.
- Improve the efficiency and scalability of the Forecasting System.
- Monitor the forecasts and key forecast performance metrics, understanding the root causes of changes in the forecast and metrics as a core part of continuous improvement and customer communication.
- Work closely with business leads and experts in Global Planning, other Supply Chain functions, Finance, Product Management, Sales, and other Cisco organizations to understand, discover, and characterize relationships and patterns between Cisco demand and its relation to product, technology, lifecycle, supply, customer, market, competitor, sales behavior, and macro factors.
- Engineer model features using these factors, discover and enhance the natural segmentation for Demand based on these factors, determine the causality of the factors, and incorporate them into structured causal models.
- Develop and evolve Dashboards to expose key insights from the causal Forecasts and their drivers and deliver to customers.
- Provide integrated, reconciled, logically sound evidence-based views for different facets of Cisco's short and long-term demand.
- Develop and evolve 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 partners 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 different elements of this system to improve forecast accuracy and incorporate learnings from formal and informal collaborations with team members and other guides into the AI system.
- Work with our AI vendors to enhance their platforms to improve causal inference-based forecasting, team member engagement, and decision support.
- Provide technical direction and coaching to less experienced data scientists and data engineers in the team, and to interns and for collaborations with Universities.
QUALIFICATIONS:
- 4+ years of Advanced Analytics experience with a Masters degree or equivalent, or 2+ years with a Ph.D. in a Quantitative field leveraging statistical and machine learning methods in the thesis.
- All-round foundation in AI and machine learning, with a theoretical and practical understanding of Causal machine learning approaches.
- Expertise in Python, with advanced data analysis and data engineering skills, including using SQL
- Expertise and understanding of statistics and causal inference in time series settings
- Critical thinking, with a sharp eye for patterns and the skills to draw out the story and conclusions from data and modeling experiments in real-time.
- Experience with global financial markets, macro-economics, micro-economics, econometrics, and Corporate Finance preferred
- Experience in developing and operationalizing scalable ML solutions in cloud environments.
- Demonstrated structured wrangling and mining skills from data, and problem-solving skills using machine learning, including in real-time hackathon-like settings.
- Good communication and storytelling skills with an ability to unpack complex problems, and articulate AI/ML approaches, solutions, and results for non-technical audiences.
