A cutting-edge biotech company is seeking an experienced Machine Learning Scientist with expertise in data mining to analyze pre-clinical and/or clinical data. This role will focus on uncovering novel protein targets and identifying opportunities for drug optimization in immunotherapy for cancer and other diseases.
Seeking a Machine Learning Scientist with a strong background in data mining, machine learning, and computational biology. The ideal candidate will work with large datasets from public and proprietary sources, leveraging AI-driven analytics to support drug discovery and optimization efforts.
Key Responsibilities:
- Data Mining & Analysis: Utilize advanced data mining techniques on clinical and pre-clinical datasets to identify trends, patterns, and correlations.
- Machine Learning Model Development: Design, develop, and train machine learning models for classifying and processing research data to enhance drug discovery efforts.
- Protein Target Identification: Apply AI, computational biology, and bioinformatics methodologies to identify new protein targets for immunotherapy.
- Interdisciplinary Collaboration: Work closely with research teams, clinicians, and other stakeholders to align data-driven insights with scientific and business objective
- Results Interpretation & Communication: Present findings through reports, visualizations, and presentations for both technical and non-technical audiences.
Requirements
- Master’s or Ph.D. in Computer Science, Machine Learning, Biostatistics, Bioinformatics, or a related field.
- 3+ years in machine learning, data mining, or related fields, with a focus on clinical trial data analysis.
- Proficiency in Python, R, or SQL
- Experience with machine learning frameworks such as TensorFlow or PyTorch.
- Familiarity with data visualization tools like Tableau, Power BI
- Strong understanding of clinical trial design, biostatistics, and immunotherapy
- Ability to convey complex data insights effectively to interdisciplinary teams.
Preferred Qualifications:
- Experience with containerization (e.g., Docker)
- Understanding of data security and compliance regulations (e.g., HIPAA)
- Familiarity with agile development methodologies and version control systems (e.g., Git).
