Positions Available: Statistical Learning

We are planning large-scale single-cell and population genomics sequencing studies and are looking for a motivated and driven masters or PhD level candidates with experience in statistical learning and advanced modeling methodologies to join out team.

You will work closely with research and clinical scientists to analyze large scale genome data generated in our group with the purpose to:

• Test and develop robust analytical methodologies for data evaluation (derived from sparse and noisy population level or single cell derived genomic analysis).
• Develop unsupervised classification methods using molecular and clinical variables.
• Study genotype correlations to diagnostic variables, disease morphology and outcome endpoints.
• Develop integrative prognostic and predictive models that consider demographic, genomic clinical and intermediate therapeutic intervention steps.

Roles and Responsibilities:

• Responsible for curating and merging complex datasets and work with scientists in group to understand relationships between variables and resolve data inconsistencies.
• Conduct literature review, understand and implement best analytical practices statistical analysis and interprets the results relative to literature and findings.
• Write and run programs for data analysis, using R and create production level annotated code.
• Conducts sophisticated statistical analysis and interprets the results requiring extensive knowledge of biostatistics.
• Generate quality control reports of data, analysis and results.
• Actively participate in project design, method selection, method development and presentation of results to team and department.
• Presents research findings on conferences, and publishes articles in peer reviewed research journals.

Qualifications

• Bachelor’s degree in Statistics, Mathematics, Biostatistics or related field.
• At least 2 years experience, or applicant must represent top 5% in class with references in strong support of minimum requirements listed below.
• Independent on quantitative analysis and data interpretation.
• Experience with mathematical modeling and programming.
• Excellent organizational skills, ability to meet deadlines.
• Strong interpersonal and teamwork skills
• Strong oral and written communication skills.

Alternative career paths may be considered for candidates with work experience or equivalent qualifications. For more information contact Elli Papaemmanuil at [email protected]

Positions Available: Postdoctoral Positions — Precision Medicine

We are expanding our program in pediatric oncology and are seeking outstanding postdoctoral candidates with a background in computational biology, experience in next generation sequencing analysis and an interest in cancer genomics to work with a strong network of research and clinical investigators, as well as bench scientists in the lab to perform whole-genome sequencing analysis of cancer patients with excellent clinical annotation.

The applicant will work closely with the computational oncology service at Memorial Sloan Kettering Cancer Center (MSK) to analyze high-quality genome profiling data from diagnostic samples. The generated data will be used to drive the development of statistical models that integrate related and orthogonal datasets (genomic, transcriptome, demographic and clinical variables) to delineate refined ontologies in cancer and deliver comprehensive and personalized prognostication models of disease.

The applicant will use state of the art genome sequencing technologies to elucidate the molecular mechanisms that underpin cancer pathogenesis in cancer, mechanisms of disease progression and treatment response.

Responsibilities:

• Become knowledge expert on field of research
• Ensure generation of high-quality genome profiling data and integration of large complex datasets for analysis.
• Develop innovative analytical methods to derive biological and clinical insights from complex genome profiling datasets.
• Develop ideas and drive own independent research, present findings I meetings and submit research for peer review and publication.
• Work independently and as a team member contributing towards collaborative research initiatives in the laboratory.

Qualifications:

• PhD in Computational Biology, Biostatistics, Mathematics or a related quantitative field (i.e. Statistical Genomics, Physics, Computer Science, Bioinformatics, Statistics).
• Demonstrated experience in applied data science approaches to the analysis of complex and heterogeneous datasets.
• Knowledge of NGS derived data types and related technologies.
• Proficient in R and in one or more programming languages (Perl, Python, C).
• Good written and oral English communication skills.

Alternative career paths may be considered for candidates with work experience or equivalent qualifications. For more information contact Elli Papaemmanuil at [email protected]