BACKGROUND

RESPONSIBILITIES

• Extract, manipulate, transform, and analyze large, diverse RNA and DNA data sets emanating from the cancer gene discovery and clinical validation efforts
• Utilize understanding and experience in assessing data quality and data normalization for gene expression and DNA polymorphism data
• Apply knowledge of classification methods to generate gene signatures for disease prediction
• Apply skills in advanced mathematics, logic, and statistics to translate these concepts to algorithm development
• Evaluate data collected as part of ongoing clinical trials as well as mining of data submitted to and available from the FDA
• Structure clinical trials, specifically in powering sample sets to an adequate level to achieve statistically significant robust biomarkers
• Work effectively in a highly diverse team-oriented environment, including experimental research and development scientists, computer programmers, marketing personnel and potential customers and clinical collaborators
• Create statistical analysis plans
• Create statistical text for study concept documents and protocols (study design, endpoints, sample size, methods for analysis)
• Develop and maintain standardized statistical approaches and methodology
• Utilize independent project management skills to ensure timely and reliable results
• Direct the work of one or more junior employees to achieve timely project deliverables

REQUIREMENTS

• PhD in Statistics/Biostatistics/Mathematics with an emphasis on biologically related systems
• 7-10 years experience in the field with a proven successful track record in analysis of highly complex and large genomic datasets and algorithm development
• Track record of managing 1 or more subordinates as well as demonstrated project management success
• Programming Languages: Fluency in C/C++, Visual C++, C#, STL, MFC, ATL, COM, ADO, DAO, OLEDB, Visual Basic, R, S, Matlab, Shell scripts, Perl and Latex
• Statistics: statistical modeling, linear programming, linear regression analysis, parametric and non-parametric techniques, principal components analysis and ANOVA etc.
• Mathematics: mathematical modeling, numerical analysis
• Mathematical and Statistical Software Packages: R, Matlab, S-PLUS.
• Other Software Packages: MS Access, MS Excel, SQL Server, SourceSafe, CVS, EMACS etc.
• OS: Windows, UNIX, LINUX
• Databases: Oracle/SQL/mySQL
• Demonstrated experience in software engineering
• Critical understanding of Genomics and System Biology including one or more areas such as gene expression profiling, human DNA variation, proteomics and pathway analysis
• Working knowledge of data visualization software, such as Spotfire and PartekHands-on experience with web technologies/packages such as Apache, Wiki, XML, HTML, ASP/JSP/PHP (if we think this person will need to write some web-based software for the product)
• Good knowledge of object-oriented programming
• Understanding of biological variability, technical variability, and method variability inherent in microarray and genomic data.
• Industry experience preferred
• Oncology data analysis experience a plus
• Experience in biomarker discovery and selection using gene expression or DNA polymorphism data a plus

• Excellent problem solving skills; demonstrated strength in tact and diplomacy working with external and internal collaborators
• Ability to work independently; exercise good judgment
• Excellent teamwork and collaboration skills
• Excellent written and oral communication skills
• Excellent interpersonal skills

LOCALE

COMPENSATION

HOW TO APPLY