Data Scientist AI Computational

Summary

UT MD Anderson Cancer Center is seeking an AI-driven Data Scientist to support its A3D3a adaptive, AI-augmented drug discovery and development platform. The role develops and deploys machine learning and deep learning tools, analyzes integrated cancer patient data, and collaborates with biologists, data scientists, and clinicians to identify therapeutic opportunities and produce scientific publications and grant materials.

Responsibilities

• Build data science tools and predictive algorithms focusing on state-of-art machine learning models
• Develop novel deep learning approaches and utilizing Large Language Models and Graph Neural Networks for cancer data
• Maintain knowledge of cutting-edge machine learning approaches and technologies and implement these where appropriate
• Perform data wrangling and preparation for deep learning analysis
• Perform statistical analysis
• Produce output for scientific publications and co-author said publications
• Prepare written reports, manuscripts, and grant applications with investigators

Skills

• Machine Learning (e.g., Naïve Bayes, Random Forests, Support Vector Machines, etc.)
• Deep Learning (e.g., Convolutional Neural Networks, Graph Neural Networks, Autoencoders, etc.)
• LLM (fine-tuning, multi-agents)
• Addressing challenges in Machine Learning / Deep Learning as well as mitigation strategies including data bias, imbalance, and model validation approaches
• Machine-learning platforms (e.g. TensorFlow, sklearn, Keras, etc.)
• Unix, Python, R/Matlab, or other scripting/programming languages
• Excellent working knowledge of statistical methods and tests
• Required: Bachelor's Degree Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field
• Required: Three years scientific software or industry development/analysis experience. With Master's degree, one year experience. With PhD, no experience required
• Preferred: Master's Degree Science, Engineering or related field. PhD Science, Engineering or related field
• Preferred: Machine Learning, Large language model, AI Agent and/or Biomedical Research is a plus

Qualifications

Must Haves

• Machine Learning (e.g., Naïve Bayes, Random Forests, Support Vector Machines, etc.)
• Deep Learning (e.g., Convolutional Neural Networks, Graph Neural Networks, Autoencoders, etc.)
• LLM (fine-tuning, multi-agents)
• Addressing challenges in Machine Learning / Deep Learning as well as mitigation strategies including data bias, imbalance, and model validation approaches
• Machine-learning platforms (e.g. TensorFlow, sklearn, Keras, etc.)
• Unix, Python, R/Matlab, or other scripting/programming languages
• Excellent working knowledge of statistical methods and tests
• Required: Bachelor's Degree Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field
• Required: Three years scientific software or industry development/analysis experience. With Master's degree, one year experience. With PhD, no experience required

Nice to Haves

• Preferred: Master's Degree Science, Engineering or related field. PhD Science, Engineering or related field
• Preferred: Machine Learning, Large language model, AI Agent and/or Biomedical Research is a plus

Benefits

• Employer-paid medical coverage starting day one for employees working 30+ hours/week
• Optional group dental, vision, life, AD&D, and disability insurance
• Accruals for PTO and Extended Illness Bank
• Paid holidays, wellness, childcare, and other leave options
• Tuition Assistance Program after six months of service
• Extensive wellness and fitness programs
• Employee resource groups
• Defined-benefit pension through the Teachers Retirement System
• Voluntary retirement plans
• Employer-paid life and reduced salary protection programs
• Medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
• Hybrid Onsite/Remote work arrangement
• Referral bonus available
• Relocation assistance available