"Scientist in Data Science & Machine Learning"

Position Summary:

The UM6P Vanguard Center & The School of Applied Sciences & Engineering at UM6P invite applications for the position of Scientist in Data Science & Machine Learning to support the Wicked Problems (WiP) framework within our undergraduate program. The successful candidate will bring deep technical expertise and educational engagement to interdisciplinary projects requiring advanced data analysis, predictive modeling, and AI-based solutions to real-world societal challenges.

Key Responsibilities:

• Provide methodological support and design data-driven approaches for WiP student projects.
• Mentor undergraduate teams working on interdisciplinary problems involving large, complex, or dynamic datasets.
• Collaborate with teaching faculty to integrate machine learning modules into the curriculum, particularly within the WiP ecosystem.
• Co-develop tools and platforms for visualizing, analyzing, and interpreting datasets from various domains (health, agriculture, energy, logistics, etc.).
• Contribute to institutional efforts in building AI tutors, learning analytics, and knowledge graphs to enhance student experience.
• Publish research and co-author student-led publications resulting from WiP investigations.
• Contribute to applied research motivated by the WiP investigations.

Required Qualifications:

• Ph.D. in Data Science, Machine Learning, Computer Science, Applied Statistics, or a related field.
• Demonstrated expertise in supervised/unsupervised learning, deep learning, and data-driven modeling.
• Proficiency in modern ML toolkits and libraries (e.g., TensorFlow, PyTorch, scikit-learn, pandas, Jupyter).
• Experience handling diverse data types (tabular, time-series, geospatial, text).
• Strong mentoring skills and interest in education.

Desirable Qualifications:

• Experience with project-based learning or working with undergraduate students.
• Background in applied domains such as agriculture, health, manufacturing, or environmental systems.
• Interest in responsible AI, ethics in machine learning, or interpretable models.
• Familiarity with cloud-based environments or distributed computing tools (e.g., Spark, Dask, Kubernetes).