Wolters Kluwer is a global leader in professional information services. Professionals in the areas of healthcare, legal, business, tax, accounting, finance, audit, risk and compliance rely on Wolters Kluwer's market leading information-enabled tools and software solutions to manage their business efficiently, deliver results to their clients, and succeed in an ever more dynamic world.
Wolters Kluwer combines deep domain knowledge with specialized technology. Our portfolio offers software tools coupled with content and services that customers need to make decisions with confidence.
Every day, our customers make critical decisions to help save lives, improve the way we do business, build better judicial and regulatory systems.
We help them get it right.
This role is part of the Advanced Technology team in the Digital eXperience Group (* / dxg) which co-creates state-of-the art products and solutions with Wolters Kluwer businesses around the globe.
Our growing array of solutions supports the expanding set of Wolters Kluwer’s Expert Solutions and online product portfolios.
Develop predictive models using supervised and unsupervised machine learning / deep learning
Build models for forecasting and anomaly detection
Provide expertise in data wrangling, exploratory data analysis and feature engineering with large data sets
Support the development of proofs of concept to demonstrate the application of AI / ML capabilities in solving customer problems in collaboration with product and other development teams
Collaborate effectively with other teams across DXG and Wolters Kluwer
A self-starter who can align the technology solution with a business goal
Graduate degree (MS or PhD) in Computer Science, Engineering, Mathematics or equivalent, specializing in machine learning or a related field
3+ years’ experience in supporting development of production-ready solutions leveraging AI technologies : NLP, Deep Learning, Machine Learning
Strong hands-on expertise in Python and open source libraries / frameworks / tools such as NumPy, SciPy, scikit-learn, pandas, matplotlib, spaCy, NLTK, jupyter, anaconda, transformers, etc.
Experience with deep learning frameworks such as TensorFlow, Keras, PyTorch
Experience in applying deep learning to NLP
Computer vision modeling a plus
Experience with designing, building, and optimizing data and model training pipelines
Experience in MLflow or SageMaker a plus
Experience with cloud-based platforms (AWS or Azure) for solution delivery
Experience working with agile and Software Development Lifecycle tools (e.g. JIRA, Confluence, Git) and Test-Driven Development (TDD)