Description

As a Computational Scientist in Oncology, you will collaborate with a team of experts to drive data strategies, foster a culture of feedback and improvement, and generate insights that enhance the competitive edge of Computational Biology in the oncology field.

Requirements

• •PhD in a scientific or technical field
• •Experience in disease biology with expertise in Oncology
• •Technical expertise in population statistical genetics, integrative multiomics, and AI/ML applications
• •Matrix leadership with evidence of high-performing teams
• •History of building effective relationships in research ecosystem
• •Evidence of shaping and executing strategy
• •Strong communication, influence, and leadership skills
• •Expertise in biological data analysis for decision-making
• •Process-oriented with a cost-conscious mindset
• •T-shaped skills portfolio with broad and deep experience
• •Programming/scripting skills in R and/or Python
• •Working knowledge of Linux/Unix and data processing in HPC
• •Experience working with wet-lab scientists and managing projects
• •Strong statistical, reporting, and data visualization skills
• •Expertise in handling and visualizing multimodal data
• •Algorithm development and statistical analysis of large datasets
• •Expertise in genetics, single-cell, and metabolomics
• •Experience leveraging clinical datasets for disease understanding

Tasks

• •Execute the data and analytics strategy
• •Support team members' knowledge and skills
• •Foster a rich feedback culture
• •Promote continuous improvement methodologies
• •Encourage an inclusive and collaborative workplace
• •Represent Computational Biology to stakeholders
• •Develop relationships with key stakeholders
• •Generate insights to inform strategy
• •Deliver high-value data insights for the portfolio
• •Prioritize critical activities with stakeholders

Tools & Technologies