2021 Data Science Interns
Recilience360 enables companies to visualize, track, and mitigate risks in their supply chain. The Recilience360 suite of solutions enables intuitive visualization of supplier networks, tracks shipments across different modes and lanes, and permits near real-time monitoring of incidents capable of disrupting supply chains.
Born at the world’s largest logistics company DHL and incubated in their Global Innovation Center, Resilience360 provides an end-to-end supply chain risk assessment and monitoring solution. Customers trust Resilience360 to ensure business continuity, identify critical hotspots to mitigate risks, and turn potential disruptions into a competitive advantage. Resilience360 provides companies a first mover advantage in detecting and verifying risks using both Artificial Intelligence and a human network of DHL employees in 220 countries and territories. Find out more at www.resilience360.dhl.com.
Resilience360 has recently become an independently operated company under the holding company, Rising Tide Digital, following an investment by Columbia Capital in 2018. Columbia Capital is a Washington, DC-based venture capital group with a long history of investing in and scaling high growth and disruptive technology companies. In January 2020, Columbia Capital, Greenspring Associates and DHL announced to jointly acquire Riskpulse. Riskpulse is designed to help supply chain managers, carriers, distributors, and their shippers increase their on-time performance, reduce unnecessary freight spending, and avoid waste caused by operational and natural, social or infrastructure-driven variability.
Thanks to our remarkable people we are at the forefront of change and bringing cutting-edge products and services to market. We cultivate a culture where resiliency, responsiveness and critical thinking are integrated into every aspect of what we do. If you share in our passion to revolutionize the supply chain industry with disruptive technology, we want you to fast-forward your career at Recilience360.
JOIN OUR DATA SCIENCE TEAM
Resilience360 is looking for several interns to work with our Data Science team. As a member of Data Science, you will join a dynamic team of people focused on our next generation product. This is an exciting opportunity to help shape how we use data to build models and generate actionable insights to give our customers a risk aware supply chain which will advance outcomes for the business and the industry. Interns will work closely with many members of the data science team.
Preferred Knowledge & Background
- Proficiency in Python is required
- Familiarity with ML modeling techniques, preference given to those with an understanding of any of the following: Gradient Boosted Modeling (GBM), Percentile Regression, NLP, Entity Resolution
- Understanding of ML training and testing procedures
- Knowledge of probabilistic and statistical learning methods
Knowledge & Skills To Be Gained
While working as an intern with the R360 data science team you will have the opportunity to advance your skills in:
- Data Wrangling – merging and building features in 100+GB datasets
- Exploratory Data Analysis – conducting exploratory analysis to extract insights and expose important groups, trends, and relationships in data.
- Modeling – training and testing models on new data
- Analyzing and Reporting – evaluating business questions and summarizing conclusions to stakeholders
- Project Administration – incorporating work into larger project and using programming best practices (e.g. project timeline/tasks, code annotation and reviews, etc.)
Interns will get exposure to a wide range of datasets including shipping, supplier, weather, news and incident data. They will work with the team to build models to solve real industry problems that will be deployed in a real production software environment where the model performance is really important.
- Combining historic weather and shipment dataset to build training data for model building.
- Researching and applying different entity resolution techniques for knowledge graphs.
- Analyzing production data to identify potential new features that are characteristic differences in customer shipments and behaviors.
- Gathering and combining incident and news in multiple languages to build training/test datasets.
- Researching similarity and learning-based entity resolution techniques to create a holistic data picture of a company’s supply chain.