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Network Data Scientist

4 months ago


London, Canada Altana AI Full time

Brooklyn NY; San Francisco, CA; London, England

Altana provides the world’s only dynamic, intelligent map of the global supply chain - the Altana Atlas - using AI and machine learning models to connect with and learn from massive sets of public and private data. Through the Atlas, companies and governments can understand the distant origins of products well beyond their own direct suppliers; discover trading relationships and national security risks deep in their networks; measure labor and environmental impacts; identify related risks and opportunities; ensure effective compliance and enforcement with trade requirements; and collaborate to manage all of it. 

We have built a fundamental understanding of how the world’s economy works, and the implications for global resiliency, sustainability and opportunity are enormous. Backed by leading investors and used by the world’s most important organizations (Maersk, US Customs and Border Protection, Boston Scientific, and more), Altana’s mission is to power a new era of globalization organized around trusted supply chain networks.

This is a lofty mission, and our success depends on building a diverse, global team and creating an environment in which they can thrive. We operate in accordance with our values : we focus on value creation, not capture; we foster diversity and embrace difference; we embrace reality; we get things done; we amaze our clients. When you join Altana, you’ll be joining a vibrant, collaborative team working together to solve complex problems with the potential for global societal impact. 

The Opportunity at Altana

The Financial Services team is looking for talented Network Data Scientists to help execute this vision. You’ll work closely with our Clients, Data Scientists, Product Managers, Software Engineers, and leadership team to develop analytics that provide insights on network dynamics and their impacts on financial performance in supplier networks.

Responsibilities

  • 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 analytical 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 businesses in the network 
  • Develop models to describe, predict, and simulate network behavior under multiple scenarios, including scenarios that change over time 
  • Categorize relationships between 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 network analytics
  • Facilitate usage of analytical capabilities though architectural and interface design

About You

  • BS, MS, or PhD degree in Computer Science, Data Science, Economics, Statistics, Computational Biology, BioInformatics, or equivalent experience
  • You have a strong background in data science, graph mining, and graph simulations
  • You are skilled in software architecture; designing and implementing clean code and interfaces
  • You enjoy sharing your expertise in world-scale graph analysis and machine learning
  • You have experience building data intensive applications that model complex systems
  • You like working with systems that exhibit power laws, long-tail effects, and nonlinear system dynamics 
  • 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

Nice to have, but not required

  • Experience with Apache Spark, Databricks
  • Experience with agent-based models of network dynamics
  • Experience with generative models of network structure and behavior
  • Experience analyzing supplier networks in supply chains, banking, insurance, or ecommerce

Technologies we love

Languages: Python, Spark, SQL

This role can be based in New York, San Francisco or London (UK) and candidates must be within commuting distance from our Hub offices.

Why it’s great to work at Altana
  • We love to collaborate, and we win as a team
  • We are committed to engineering excellence
  • We value personal and professional development
  • We learn from diverse backgrounds and perspectives
  • We impact the world, from enabling developing countries to identifying drug traffickers

At Altana, we believe that a diverse workforce enables greater creativity, performance, and adaptability. We’re proud to be an equal opportunity employer and welcome you to join us as you are. Our employment opportunities and decisions are based on business needs and individual qualifications, without regard to race, color, religious creed, national origin, ancestry, age, physical or mental disability, medical condition, marital status, sexual orientation, gender identity or expression, genetic information, family care or medical leave status, military or veteran status, or any other characteristic protected by the laws or regulations in the areas in which we operate. We prohibit discrimination and harassment of any type, in any situation.

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As set forth in Altana AI’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection.As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows:

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