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Cisco Lead / Senior Applied Data Scientist United Kingdom, England, London 940328861
06.03.2025
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.
- 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 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.
- 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.
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