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Jessica Danielle Geiger

Data Scientist | Drug Discovery & Machine Learning | Life Sciences

(831)-801-9453 | daniellegeiger99@gmail.com | GitHub: github.com/danigeiger | LinkedIn: www.linkedin.com/in/jessica-d-geiger

Data Scientist | Drug Discovery & Machine Learning (Life Sciences)

Master’s-trained Data Scientist with a background in human physiology and experience in pharmaceutical R&D. Specialized in applying machine learning to biological and chemical data, including predictive modeling for drug discovery. Experienced in building end-to-end data pipelines, feature engineering molecular data, and deploying models via interactive applications.

TECHNICAL SKILLS

Programming & Data
- Python, R, SQL
- Pandas, NumPy
- Git/GitHub
- scikit-learn, TensorFlow

Machine Learning & Statistics - Exploratory Data Analysis (EDA)
- Statistical analysis & data validation
- Data preprocessing & feature engineering
- Regression & classification models
- Ensemble methods
- Feature selection & PCA

Visualization Tools
- Streamlit
- Excel
- Quarto
- matplotlib

Cloud Tools
- AWS EC2
- AWS S3
- AWS Athena

EXPERIENCE

Aromas-San Juan Unified School District
Substitute Teacher (Aug 2023 – Present)

  • Designed and delivered curriculum tailored to diverse learning levels, ensuring comprehension of key concepts across subjects
  • Assessed student understanding through formative evaluations and adapted instruction to improve learning outcomes
  • Collaborated with faculty and staff to maintain continuity of instruction and foster a positive classroom environment
  • Developed strong organizational, classroom management, and problem-solving skills in fast-paced settings

AstraZeneca
Associate, R&D (Sep 2022 – Jun 2023)

  • Supported biologic drug formulation in a cGMP environment, contributing to batch production for clinical trials
  • Maintained and analyzed batch data, process parameters, and quality metrics to ensure consistency, regulatory compliance, and reproducibility
  • Monitored spray-drying operations, identifying relationships between process variables and product outcomes to improve formulation performance and stability
  • Collaborated with directors and process engineers to troubleshoot variability and meet target specifications, enabling development of a safe and effective clinical formulation

PROJECTS

CGRP Drug Discovery Capstone Project (Github)

  • Developed an end-to-end machine learning pipeline to predict small molecule inhibitory activity (IC50) against the CGRP receptor using bioactivity data from ChEMBL
  • Engineered 800+ molecular fingerprint features and applied variance threshold feature selection and PCA
  • Built a reproducible preprocessing pipeline and deployed a Streamlit application for real-time predictions

EDUCATION & CERTIFICATIONS

  • AWS Certified Cloud Practitioner
  • M.S. Data Science – Eastern University (2025)
  • B.S. Human Physiology (Minor: Biochemistry) – University of Oregon (2020)