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

Cisco’s Forecasting Data Science Team within Global Planning is developing a Causal AI-based system for aggregated demand forecasting. This senior data scientist will help improve forecast accuracy, scenario planning and decision-making across Cisco’s enterprise and supply chain. The position is full-time and hybrid in Bangalore, India, with a stated schedule of 40 hours per week.

Responsibilities

• Develop and sustain components of the forecasting system, building accurate, robust models that remain useful over time.
• Assess how global financial markets, macroeconomic, microeconomic and competitive factors affect demand. Engineer features from internal and external structured and unstructured data; refine demand segmentation; establish causal relationships; and incorporate factors into structural causal models.
• Investigate structural causal modelling problems and adapt relevant machine learning research for enterprise and supply chain applications.
• Develop uncertainty-quantification methods for scenario and range forecasts, and research ways to reconcile forecasts across product hierarchies, time horizons and forecasting approaches.
• Collaborate with Global Planning, supply chain, Finance and other business experts to understand demand drivers. Provide technical direction and mentor junior data scientists and data engineers.

Minimum qualifications:

• 6+ years of advanced analytics experience with a master’s degree, or 4+ years with a PhD, in mathematics, applied mathematics, operations research, economics, econometrics, physics, computer science, engineering or a related quantitative field.
• Theoretical and practical grounding in AI, machine learning and causal machine learning; expertise in Python; strong data analysis and data engineering skills using SQL; and experience with Git version control.
• Demonstrated structured-data wrangling, data mining and ML problem-solving skills, including in real-time hackathon-like settings. Strong communication and storytelling skills for explaining complex approaches and results to non-technical audiences.

Preferred qualifications

• Experience with financial markets, macroeconomics, microeconomics, econometrics and financial datasets; causal AI and structural causal models for time series; and demand forecasting or other complex domains such as marketing or pricing.
• Ability to translate business needs into feasible analytics solutions, plus team leadership, project management, business-partner influencing and mentoring experience.