Original Beschreibung
# Senior Scientist: Computational Biology - ICVRM
**Basel** | **Full time**
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### The Position
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
In Roche´s Pharma Research and Early Development organisation (pRED), we make transformative medicines for patients in order to tackle some of the world’s toughest unmet healthcare needs. At pRED, we are united by our mission to transform science into medicines. Together, we create a culture defined by curiosity, responsibility and humility, where our talented people are empowered and inspired to bring forward extraordinary life-changing innovation at speed.
The Computational Sciences Centre of Excellence is a global organisation enabling Roche’s Research and Early Development units to become more data-driven, more digitally adept and better prepared for the challenges of the future. Within the Computational Sciences Centre of Excellence, the Computational Biology department focuses on furthering our understanding of disease, patient populations, and target biology by having access to the largest Pharma R&D datasets in the world. In partnership with our scientists across pRED, we create better medicines by augmenting every part of R&D through our data-driven culture.
### The Opportunity:
This position will ensure that pRED is supported and enabled with world-leading computational biology capabilities. You will contribute to the Cardiovascular, Renal, Metabolic and Immunology disease sub-unit of Computational Biology (CB-ICVRM) responsible for our contribution to the portfolio. Together with other computational biology experts you will form CVRMI unit with the express aim of developing a competitive advantage for pRED through the application of computational biology. You will be part of the wider Computational Biology and Medicine unit of the <<Computational Sciences Centre of Excellence>> drawing on the shared experience and expertise of colleagues globally. Where necessary to deliver for the portfolio you collaborate with other subunits in Computation Biology and/or Computational Medicine. The CVRMI subunit is collectively accountable for delivering for the portfolio. Your success is measured by the success of your team and the contribution to the pRED/Pharma goals.
### In this role you will:
* Execute the data and analytics strategy for your subunit in collaboration with your primary stakeholders.
* Supporting and broadening team members' knowledge, capabilities, and skills
* Commit to fostering a rich feedback culture, the development of challenger safety, a continuous improvement methodology, and an inclusive, purpose-driven, and collaborative workplace.
* Represent Computational Biology as a trusted partner to our wider stakeholder ecosystem.
* Develop collaborative relationships with critical stakeholders focussing on shared objectives.
* Generate objective insights that inform and influence a continuously evolving strategy designed to ensure Computational Biology is a competitive advantage for pRED.
* Deliver for the portfolio, and for patients, through high-value and objective data insights.
* Prioritise, together with stakeholders, the most critical and highest value activities.
### Who you are:
* PhD in a scientific or technical field with 2+ years of experience in pharmaceutical R&D with deep knowledge in bioinformatics and biostatistics, data science, or related field. You have a deep understanding of disease biology especially in CVRMI diseases.
* You have technical expertise in one of more of the following domains: population statistical genetics, integrative multiomics and longitudinal disease modelling, network and/or AI/ML applications in disease biology.
* A matrix leader with demonstrable evidence of contributing to high-performing and multidisciplinary teams.
* You have a history of building long-standing, effective, collegiate, and business-oriented relationships across the enterprise and in the wider research ecosystem.
* You have demonstrable evidence of shaping, executing, and delivering on strategy.
* Strong ability to communicate, influence, and lead effectively without formal authority in complex environments. Capacity to navigate complexity, extract critical insights, and adapt to emerging situations.
* Expertise in using biological data analysis to impact decision-making along the value chain; demonstrated ability to quickly grasp topics, formulate problem statements, and achieve successful outcomes
* You operate in a process-oriented way with a cost-conscious mindset.
### Your Toolbox:
You demonstrate a T-shaped skills portfolio with broad experience and deep understanding of specific domains.
* Programming/scripting skills in R and/or Python is required. Knowledge of other programming platforms is a plus;
* Working knowledge of Linux/Unix, with experience in data processing in an HPC cluster environment and basic understanding of computer systems administration;
* Experience of working alongside experimental ‘wet-lab’ scientists, and managing several concurrent projects with changing priorities;
* Strong statistical, reporting, and data visualisation skills. Data, code, and project hygiene are essential.
Your domain expertise may include some of the following:
* Expertise in the handling, integration and visualisation of multimodal data from human clinical and/or model systems.
* Algorithm development, data mining, and statistical analysis of large datasets (including approaches such as Bayesian statistics, Markov models, simulation models, graph/network exploration methods, time-series data, and machine learning).
* Genetics/single-cell/metabolomics
* Experience in interpreting complex trait genomics data (e.g. GWAS, QTL data).
* Experience in leveraging clinical datasets (genomics, transcriptomics, proteomics, metabolomics) for understanding disease trajectories and elaborating novel therapeutic hypotheses.
# ComputationCoE
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