Cisco Senior Applied Data Scientist 

United States, Georgia, Atlanta 

699729941

Develop, evolve, and sustain key elements of the Causal-AI based Forecasting system for Aggregated Demand.

Excel in developing high quality, accurate, parsimonious 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 root causes of changes in the forecast and metrics as a core part of continuously 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 causality of the factors, and incorporate into structured causal models.

Develop and evolve Dashboards to expose key insights from the causal Forecasts and their drivers to accelerate and continuously improve the solution and increase stakeholder engagement and adoption.

Provide integrated, reconciled, and 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 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 different elements of this system to improve forecast accuracy and incorporate learnings from formal and informal collaborations with stakeholders and other experts into the AI system.

Work with our AI vendors to enhance their platforms to improve Causal Inference based forecasting, stakeholder 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.

Extensive Advanced Analytics experience with a Masters degree or some experience with a Ph.D. in a Quantitative field leveraging statistical and machine learning methods in the thesis.

Strong all-round foundation in AI and machine learning, with a theoretical and practical understanding of Causal machine learning approaches.

Proven modeling skills that have delivered an effective predictive solution to solve a business problem with minimal supervision

Expertise in Python, with advanced data analysis and data engineering skills, including using SQL

Strong Computer Science foundation

Strong 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 in developing and operationalizing scalable ML solutions in cloud environments based on large datasets.

Demonstrated structured data wrangling and mining skills that extract actionable insights from data, including in real-time hackathon-like settings.

Practical knowledge of the advantages and pitfalls of different machine learning approaches, as well as a strong grounding in the theoretical foundations

Excellent communication and storytelling skills with an ability to unpack complex problems, and articulate AI/ML approaches, solutions, and results for non-technical audiences.

Strong growth mindset and sense of ownership. Innate passion and curiosity to understand and improve the system and connect the dots.

Advanced Analytics experience with a Masters degree or experience with a PhD in Statistics, Mathematics or Applied Mathematics, Physics, Engineering, or related quantitative field.

Experience with global financial markets, macro-economics, micro-economics, econometrics, and Corporate Finance.

Substantial experience using Causal AI and Structured Causal Models in Demand Forecasting and ideally also in other complex or dynamic domains like marketing/pricing.

Practical expertise and deep understanding of statistics and causal inference in time series settings.

Experience with NLP, Recommender Systems, and Deep Learning methods.

Understanding of Gen AI/LLMs including RAGs and fine-tuning, and Reinforcement Learning.

Experience in visualization design and development with Python based libraries.

Project management skills, with an ability to deliver results in a fast-paced environment.

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.

A strong bias for action, delivering iterative results quickly rather than waiting for perfection.