Main responsibilities:
- Develop and implement machine learning / deep learning models to solve complex problems in drug discovery, clinical trials, and personalized medicine.
- Collaborate with cross-functional teams, including biostatisticians, bioinformaticians, and clinical researchers, to understand business requirements and translate them into data-driven solutions.
- Design and conduct experiments to evaluate model performance and iterate on model improvements.
- Analyze large biomedical datasets to extract meaningful insights and identify trends.
- Deploy and maintain machine learning / deep learning models in production environments, ensuring compliance with regulatory standards.
- Stay up to date with the latest advancements in deep learning, AI technologies, and pharmaceutical research.
- Communicate findings and recommendations to stakeholders through reports, presentations, and visualizations.
About you
- Experience:
- 2+ years (Master) or 1+ years (PhD) of pharmaceutical or related industry experience
- Familiar with Git operations for project development
- Experience on computer vision projects using neural network models
- Soft and technical skills:
- Proven experience in developing and deploying deep learning models using frameworks such as TensorFlow, PyTorch, or Keras.
- Strong programming skills in Python and experience with data manipulation libraries (e.g., Pandas, NumPy).
- Proficiency in working with large biomedical datasets and distributed computing tools (e.g., Hadoop, Spark).
- Solid understanding of machine learning algorithms, statistical methods, and their applications in the pharmaceutical industry.
- Excellent problem-solving skills and the ability to work independently and as part of a team.
- Strong communication skills and the ability to convey complex technical concepts to non-technical stakeholders.
- General knowledge for different type of biomarkers and molecular biology.
- Education: Master’s or Ph.D. in Computer Science, Data Science, Bioinformatics, Statistics, or a related field.
- Languages: Excellent communication in English, both oral and written