Lead / Senior Applied Data Scientist - Causal AI for Demand Forecasting

Lead / Senior Applied Data Scientist - Causal AI for Demand Forecasting

AI or Artificial Intelligence, Big Data, Analytics, Software Development

Job Id: 1433622

Meet the Team

The post-pandemic years have exposed inherent biases and limitations in human-driven and statistical/Traditional ML-based forecasting approaches. Cisco wasn’t immune and saw a sharp increase in backlogs, inventory levels, and supply chain costs. The Forecasting Data Science Team within Global Planning is solving this by using Causal AI to redefine Demand Forecasting and its Enterprise impact. We’re working to provide breakthrough levels of regime-resilient forecast accuracy, efficiency, and prescriptive insights that enable decision makers across Cisco and its Supply Chain to plan effectively.

We are a bright, engaged, and friendly distributed team working with an industry-leading Causal AI ecosystem. Gartner has ranked Cisco’s Supply Chain to be #1 or #2 in the world over the last 5 years, and recognized this team in their Power of Profession 2024 Supply Chain awards as one of the top 5 in the Process and Technology Innovation category.

Your Impact

You will bring your skills, experience, and innovation to play a significant role in shaping our Causal AI-based forecasting system to improve decision making and drive operational performance and efficiency across Cisco’s Enterprise and Supply Chain functions.

You Will

• Develop, evolve, and sustain key elements of the Causal-AI based Forecasting system for Aggregated Demand.
• Analyze and sharpen the causal consideration of global financial markets, macro-economics, micro-economic and competitive factors in the Demand Forecasting models.
• Engineer model features from broad internal and external structured and unstructured datasets, discover and improve the natural segmentation for Demand based on these factors, determine causality of the factors, and incorporate them into structural causal models.
• Develop high-quality, accurate models that are robust and have a long shelf life.
• Solve complicated research problems that push the boundaries of structural causal modelling and scale to Enterprise and Supply Chain business applications.
• Work closely with business leads and experts in Global Planning, other Supply Chain functions, Finance, and other Cisco organizations to understand relationships and patterns driving Cisco demand.
• Develop and evolve reliable approaches for uncertainty quantification to enable scenario/range forecasts.
• Research and develop new methods to reconcile between forecasts at multiple product hierarchy levels, multiple time horizons, and different forecasting approaches.
• Leverage and incorporate appropriate machine learning approaches including customization of recently published research as needed to build better Causal AI solutions.
• Provide technical direction and mentoring to junior data scientists and data engineers in the team, helping shape the skills and values of the next generation of Cisco data scientists.

Minimum Qualifications

• 6+ years of Advanced Analytics experience with a Masters Degree or 4+ years with a PhD in a Mathematics or Applied Mathematics, Operations Research, Economics, Econometrics, Physics, Computer Science, Engineering, or related quantitative field.
• Strong 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, experience with git version control.
• Demonstrated structured wrangling and mining skills from data, and problem-solving skills using machine learning, including in real-time hackathon-like settings.
• Excellent communication and storytelling skills with an ability to unpack complex problems, and articulate AI/ML approaches, solutions, and results for non-technical audiences.

Preferred Qualifications

• Experience with global financial markets, macro-economics, micro-economics, econometrics, and financial datasets.
• Substantial experience using Causal AI and Structured Causal Models in time series settings.
• Substantial experience in time series forecasting for demand use cases and/or other complex or dynamic domains like marketing/pricing.
• A practical and effective approach to problem-solving using AI/ML and a knack for envisioning, translating business requirements into analytics requirements, and realizing feasible data science solutions.
• Demonstrated team leadership, project management, and business stakeholder influencing skills.
• Experience mentoring team members to improve their own technical and project management skills.

#WeAreCisco

#WeAreCisco where every individual brings their unique skills and perspectives together to pursue our purpose of powering an inclusive future for all.

Our passion is connection—we celebrate our employees’ diverse set of backgrounds and focus on unlocking potential. Cisconians often experience one company, many careers where learning and development are encouraged and supported at every stage. Our technology, tools, and culture pioneered hybrid work trends, allowing all to not only give their best, but be their best.

We understand our outstanding opportunity to bring communities together and at the heart of that is our people. One-third of Cisconians collaborate in our 30 employee resource organizations, called Inclusive Communities, to connect, foster belonging, learn to be informed allies, and make a difference. Dedicated paid time off to volunteer—80 hours each year—allows us to give back to causes we are passionate about, and nearly 86% do!

Our purpose, driven by our people, is what makes us the worldwide leader in technology that powers the internet. Helping our customers reimagine their applications, secure their enterprise, transform their infrastructure, and meet their sustainability goals is what we do best. We ensure that every step we take is a step towards a more inclusive future for all. Take your next step and be you, with us!

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