Staff Data Scientist, Altana AI

• BS, MS, or PhD degree in Computer Science, Data Science, Economics, Statistics, Mathematics, Physics, Computational Biology or equivalent experience
• You have a strong background in data science, machine learning, graph mining, and graphical modeling algorithms
• You are skilled in software architecture; designing and implementing clean Python code and interfaces
• You enjoy sharing your expertise in analysis, mathematical modeling and machine learning
• You’re eager to learn anything necessary to do your job better
• You have experience building data intensive applications that model complex systems
• You are comfortable communicating technical analysis to less technical stakeholders
• You are interested in applying network analysis to improve the world
• You have strong written and verbal communication skills

Desirable

• The Value Chain Optimization team is looking for talented Data Scientists and Machine Learning experts to help execute this vision
• You’ll work closely with our customers, Product Managers, ML Engineers, and leadership team to perform deep data science and develop sophisticated statistical and ML modes that provide insights on network dynamics and their impacts on various metrics in supplier networks and product value chains
• Develop statistical models of network structures and their relationships to network behavior
• Automate identification of persistent network structures as well as emergent network structures and behaviors within and across industry verticals
• Build machine learning solutions for understanding network dependencies
• Establish business-focused network resilience metrics based on network attributes such as attachment, centrality, connectivity, distance, path lengths, and similarity
• Develop new metrics that describe network characteristics as relates to the performance of products in the value chains
• Develop models to describe, predict, and simulate network behavior under multiple scenarios, including scenarios that change over time
• Categorize relationships between supplier network patterns and business outcomes
• Build descriptive and predictive models of network dynamics that can be used to plan business strategies by people regardless of their expertise in graph and network analytics
• Facilitate usage of analytical capabilities though architectural and interface design