Offer summary

Qualifications:

Bachelor's or Master's degree in Bioinformatics, Computational Biology, or a related field., Strong analytical skills with experience in high dimensional data analysis., Familiarity with oncology clinical trials and real-world evidence datasets., Proficiency in programming languages such as R or Python for data analysis..

Key responsibilities:

Analyze high dimensional data from oncology clinical trials and real-world evidence datasets.

Contribute to biomarker strategy and drug combination analysis.

Support understanding of mechanisms of action (MOA) and disease subtypes.

Assist in patient stratification and indication selection for clinical development.

We are a staffing solutions and IT Solutions company that provides you with a platform to find the right fit for your desired job profile. The employment situation is constantly shifting with the changing times, and we are here to ensure that you gather the workforce that compliments your long-term goals. We understand the struggle of the search for talent that accommodates the skills and qualifications for specific profiles, one that blends in with the theme of your organization. We aim to be the best available source for young talent to find their dream jobs, by helping them narrowing down their options to the most suitable work profiles available in the market. At RIG, we consistently work towards creating the latest technology that may not only simplify your work process but also provides you with the most cost-effective solutions. We work hard to make sure that your business strives in the market to be on the top in your field.

Job description

Job Description

The successful candidate will have the opportunity to work in a fast-paced and highly collaborative environment to contribute to oncology discovery and clinical development (including analysis of for analysis of high dimensional data from oncology clinical trials, real world evidence datasets to enable biomarker strategy, drug combinations, understanding of MOA, disease subtypes, indication selection, patient stratification, etc.)

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