Experience required:
Education: A bachelor's or master's degree in computer science, biomedical engineering, pharmaceutical sciences, or a related field. A Ph.D. may be preferred for more advanced research and development roles.
Minimum of 5 years of experience in data science, machine learning, or artificial intelligence
Total 10 to 14 years of IT experience, most of the experience on Data and Analytics.
AI and programming skills: Proficiency in AI techniques, such as machine learning, deep learning, natural language processing, and statistical analysis. Strong programming skills in languages such as Python, R, or Java, as well as experience with relevant AI frameworks and libraries (e.g., TensorFlow, Keras, PyTorch).
Pharmaceutical domain knowledge: Familiarity with pharmaceutical industry processes, drug discovery, clinical trials, and regulatory requirements.
Data analysis and modeling: Experience in handling and analyzing large-scale datasets, including pre-processing, feature engineering, and modeling. Knowledge of data visualization tools and statistical techniques is beneficial.
Problem-solving and critical thinking: Ability to approach complex problems with a systematic and analytical mindset. Strong problem-solving and critical thinking skills to identify AI-driven solutions that address specific challenges in the pharmaceutical industry.
Communication and collaboration: Excellent verbal and written communication skills to effectively collaborate with multidisciplinary teams. Ability to translate technical concepts into clear and concise language for non-technical stakeholders.
Adaptability and continuous learning: Willingness to adapt to evolving technologies and learn new tools and techniques. Demonstrated commitment to continuous learning and professional development in the field of AI and pharmaceuticals.
Demonstrate excellent persuasion, negotiation, presentation, and verbal communication skills.
Experience with data platform management in Microsoft Azure, including data warehousing, data lakes, and data pipelines.
Data engineering experience and seasoned coder in the relevant languages: Python, SQL, Scala, Java etc.
Ability to work in close partnership with groups across the IT organization (security, compliance, infrastructure, etc.) and business stakeholders in the commercial organizations.
Ability to develop and maintain productive working relationships with suppliers and specialist technology providers to assemble and maintain a distinctive and flexible mix of capabilities against future requirements.
