Senior Data Scientist (Time Series + ML) | 6+ Years

Overview

Develop and sustain the Causal AI-based forecasting system for aggregated demand within Cisco's Global Planning Forecasting Data Science Team, and provide technical direction to junior data scientists and data engineers.

What you'll do

• Develop, evolve, and sustain key elements of the Causal-AI based Forecasting system for Aggregated Demand.
• Analyse and sharpen the causal consideration of global financial markets, macro-economic, 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, resolve 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 organisations 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 forecasts across multiple product hierarchy levels, multiple time horizons, and different forecasting approaches.
• Leverage and incorporate appropriate machine learning approaches, including customisation 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.

What you'll need

• 6+ years of Advanced Analytics experience with a Masters Degree, or 4+ years with a PhD, in Mathematics or Applied Mathematics, Operations Research, Economics, Econometrics, Physics, Computer Science, Engineering, or a related quantitative field.
• Great foundation in AI and machine learning, with a theoretical and practical understanding of Causal machine learning approaches.
• Expertise in Python, with sophisticated data analysis and data engineering skills, including using SQL and 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 sophisticated problems and articulate AI/ML approaches, solutions, and results for non-technical audiences.

Nice to have

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

Details

• Location: Bangalore, India.
• Distributed team working with an industry-leading Causal AI ecosystem.