Bioinformatics Lead Scientist Job @ GE Global Research,Niskayuna, NY, United States

Bioinformatics Lead Scientist Job


Posted Position Title: Bioinformatics Lead Scientist
Career Level: Experienced
Function: Engineering/Technology
Function Segment: Research - Biology
Location: United States
U.S. State, China or Canada Provinces: New York
City: Niskayuna
Postal Code: 12309-1027
Relocation Assistance: Yes
Role Summary/Purpose: GE is an equal opportunity employer, offering a great work environment, challenging career opportunities, professional training and competitive compensation.



As a researcher in the Computational Biology & Biostatistics lab, you will be part of a team of 12-15 scientists & engineers at GE GRC – Niskayuna, NY within the Biosignatures & Signal Processing. This organization is focused on research in computational biology, algorithms for novel biomarker discovery, biosignatures & design of next generation molecular diagnostics tests. The Lab partners with GE Healthcare Medical Diagnostics in the area of molecular pathology & diagnostics.
Essential Responsibilities:
 You will develop biological and disease computational models of various types with focus on next generation sequencing technologies and multi-omics approaches. You will participate in developing strategies to transition results to GE Healthcare’s product line.


You will:



  • Provide disease, modeling, and data analysis expertise to develop diagnostic assays.


  • Determine novel modeling approaches to simulate biological and disease processes.


  • Define new research areas, and guide the creation of project and grant proposals.


  • Maintain up-to-date biological knowledge in several disease fields, especially oncology, cardiology and metabolic diseases.


  • Develop transition strategies for technical work to the clinical environment and to GE Healthcare products


  • Document results and recommendations through written reports and presentations to a variety of audiences.
Qualifications/Requirements:
  • PhD in Computer Science, Statistics, Biology, Electrical Engineering, Physics, Mathematics or related field.


  • At least 2 years of post-degree experience research experience in computational biology/bioinformatics/biostatistics – industrial R&D or academia


  • Experience with Next Gen Data Analytics and building customized data analysis pipelines


  • Experience in design & development of algorithms for biomarker discovery / molecular diagnostics tests


  • Experience in Oncology and/or Neurology applications


  • Strong background in data analysis & statistical modeling of biological data


  • Strong software engineering skills in bioinformatics, experience working with –omics databases and networks


  • Proven track record leading complex technical projects


  • Awareness of global technology trends in the area of medical diagnostics including companion diagnostics, biomarker discovery, next gen sequencing and expression analysis


  • Demonstrated ability to understand customer needs and industry trends; simplify strategy into specific actions, make decisions, and communicate priorities.


  • Demonstrated ability to influence at senior levels.


  • Must be willing to work out of an office located in Niskayuna, NY.


  • Must be willing to take a drug test and submit to a background investigation as part of the selection process.


  • Must be 18 years or older.


  • You must submit your application for employment on the careers page at www.gecareers.com to be considered.
Additional Eligibility Qualifications: GE will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a background investigation and drug screen.
Desired Characteristics:
  • Experience in design and development of diagnostics tests – going from concept to product
  • Experience in data analysis of expression data – gene expression, protein expression, microRNA, etc
  • Experience in DNA data analysis-variant calling and data interpretation
  • Expertise and knowledge of nuances in the modeling of biological data
  • Experience with government funded grant proposals.