Resilinc is the industry leading provider of Supply Chain Risk Management (SCRM) solutions for the extended supply chain and is trusted by many Fortune 1000 organizations. Every year, tens of thousands of events – ranging from natural disasters to factory fires to health epidemics – shut down manufacturing and wreak havoc on global supply chains. We believe a resilient supply chain is good for everyone: it keeps product flowing, the world moving, and people in their jobs. Our supply chain risk management services enable organizations to have instant visibility into supply chain disruptions and act quickly to minimize them. Our customers include over hundreds of the world's leading organizations including IBM, General Motors, Moderna, Western Digital, and AT&T.
Resilinc is well funded (including PE investment from Vista Partners) , profitable,and shows no signs of slowing down. We run lean, work fast, and pivot quickly to accommodate our customers in order to keep supply chains moving .
About The Role
As a Sr. Data Scientist, you will be a key contributor to helping us turn years of raw data into valuable insights.You know how to build cutting edge scalable algorithms using machine learning, and most importantly, analytical brain power. In this critical role, you will:
Utilize Machine Learning algorithms to create predictive models to help customers protect their supply chains, reduce supply chain disruption losses, and proactively mitigate Supply Chain Risk.
Develop and implement advanced machine learning models like Xgboost, LSTMs for predictive analytics using tabular data.
Lead the research, design, and implementation of advanced generative models such as Transformers, GANs and VAEs.
Design and implement Graph Neural Network architectures tailored to specific link prediction tasks, leveraging libraries and frameworks like PyTorch Geometric, DGL (Deep Graph Library).
Collaborate with data engineers and domain experts to preprocess and curate knowledge graph data, ensuring data quality and integrity.
Partner with Product Management and Engineering to develop innovative models and deploy them into Resilinc's solutions.
Have a deep commitment to continuous learning, including staying updated with the latest research developments by dedicating time to reading research papers from sources such as ICLR, NIPS and AI blogs.
Help build Resilinc's Data Science team, defining processes, creating SLAs, and tracking metrics to consistently deliver Data Science solutions to Resilinc's internal teams and customers.
Qualifications
US citizenship highly preferred; US permanent resident required if not a US citizen
Master's degree in Machine Learning, Statistics or Computer Science highly preferred
Technical Skills and Capabilities
5+ years of experience in analyzing large, multi-dimensional data sets and synthesizing insights into actionable solutions
Proficiency in programming languages such as Python and experience with relevant libraries/frameworks (e.g., PyTorch,PyTorch Geometric), SQL
Experience implementing various Graph Neural Network (GNN) architectures, including Graph Convolutional Networks (GCNs), GraphSAGE, Graph Attention Networks (GATs), etc
Experience in creating relevant features and attributes from the graph structure and node attributes to provide meaningful input to the GNN models
Experience working with graph database such as Neo4j , Memgraph
Experience in deploying machine learning models to production environments
Fluent in SQL, Python, PySpark for efficient large-scale data analysis in a complicated system
Experience in fine-tuning deployed machine learning models, measure drift and build pipeline for auto-retraining
Experience explaining technical concepts and analysis implications to non-technical audiences in plain English and translating business objectives into data solutions.
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