US Tech Solutions, Inc.
Computational Biologist III(AI/ML)
US Tech Solutions, Inc., Cambridge, Massachusetts, United States, 02139
Duration: 2 months contract
While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required.
Description:
We are looking for a motivated and talented individual to join
IPSI (Immune Profiling & Systems Immunology in Immunology Discovery)
as a
Computational Scientist.
The successful candidate will work closely with stakeholders in the group to
integrate the internal single cell datasets from peripheral blood mononuclear cells
treated with multiple experimental conditions into a combined atlas. The role will focus on
applying AI/ML
and advanced computational methods to integrate
large-scale single-cell transcriptomic datasets
to benchmark and prioritize treatments and assess which assets reverse disease specific signatures.
Responsibilities :
Build a Single-Cell Atlas: Utilize already generated single-cell datasets to build a comprehensive single-cell atlas. Analyze Single-Cell Data: Assist in the
analysis of single-cell RNA-seq data
to identify disease and treatment associated regulatory networks and biomarkers. Communicate Findings: Support the team in interpreting results and presenting scientific data to both internal and external stakeholders. Collaborate with Teams: Work with cross-functional teams to leverage computational methods in therapeutic development. Impact: By building the Atlas, the candidate will enable integrating single cell experiments generated via different experimental conditions into a combined atlas. This will facilitate comparison and integration of asset signatures across experiments, allowing systematic evaluation of which asset produces the most distinct, robust, or therapeutically relevant cellular responses under various stimulation and treatment paradigms.
Preferred Skills:
Experience with
Pandas, Scikit-learn, Matplotlib, Seaborn and Scanpy . Proficiency with
Git
for version control and collaboration. Hands-on experience with
single-cell data analysis tools
(e.g., Scanpy, Seurat, Bioconductor, or equivalent). Exposure to multi-modal integration methods (e.g.,
CITE-seq, ATAC-seq,
proteomics, imaging mass cytometry, spatial transcriptomics). Knowledge of
cell type annotation ,
clustering, and trajectory inference
methods. Experience building
multi-modal AI/ML models
that link
transcriptomic, proteomic , and
imaging data.
Required Skills:
Proficiency in programming with
Python . Solid understanding of
data analysis and visualization
techniques. Experience working with
single cell RNA-seq
data. Familiarity with basic
machine learning
concepts. Domain knowledge of
Bioinformatics . Some experience in a relevant academic or industry setting is preferred. Proficiency in programming languages, particularly Python, with a solid understanding of data analysis and visualization techniques. Familiarity with omics data types (e.g.,
RNA-seq ) and basic
machine learning
concepts. Strong problem-solving skills and a desire to learn.
Education and Experience:
MS degree (5+ years of experience) or PhD (0+ years of experience) in a
quantitative field
( Bioinformatics, Computational Biology, Computer Science, Computational Biology , or related a field).
About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com.
US Tech Solutions is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required.
Description:
We are looking for a motivated and talented individual to join
IPSI (Immune Profiling & Systems Immunology in Immunology Discovery)
as a
Computational Scientist.
The successful candidate will work closely with stakeholders in the group to
integrate the internal single cell datasets from peripheral blood mononuclear cells
treated with multiple experimental conditions into a combined atlas. The role will focus on
applying AI/ML
and advanced computational methods to integrate
large-scale single-cell transcriptomic datasets
to benchmark and prioritize treatments and assess which assets reverse disease specific signatures.
Responsibilities :
Build a Single-Cell Atlas: Utilize already generated single-cell datasets to build a comprehensive single-cell atlas. Analyze Single-Cell Data: Assist in the
analysis of single-cell RNA-seq data
to identify disease and treatment associated regulatory networks and biomarkers. Communicate Findings: Support the team in interpreting results and presenting scientific data to both internal and external stakeholders. Collaborate with Teams: Work with cross-functional teams to leverage computational methods in therapeutic development. Impact: By building the Atlas, the candidate will enable integrating single cell experiments generated via different experimental conditions into a combined atlas. This will facilitate comparison and integration of asset signatures across experiments, allowing systematic evaluation of which asset produces the most distinct, robust, or therapeutically relevant cellular responses under various stimulation and treatment paradigms.
Preferred Skills:
Experience with
Pandas, Scikit-learn, Matplotlib, Seaborn and Scanpy . Proficiency with
Git
for version control and collaboration. Hands-on experience with
single-cell data analysis tools
(e.g., Scanpy, Seurat, Bioconductor, or equivalent). Exposure to multi-modal integration methods (e.g.,
CITE-seq, ATAC-seq,
proteomics, imaging mass cytometry, spatial transcriptomics). Knowledge of
cell type annotation ,
clustering, and trajectory inference
methods. Experience building
multi-modal AI/ML models
that link
transcriptomic, proteomic , and
imaging data.
Required Skills:
Proficiency in programming with
Python . Solid understanding of
data analysis and visualization
techniques. Experience working with
single cell RNA-seq
data. Familiarity with basic
machine learning
concepts. Domain knowledge of
Bioinformatics . Some experience in a relevant academic or industry setting is preferred. Proficiency in programming languages, particularly Python, with a solid understanding of data analysis and visualization techniques. Familiarity with omics data types (e.g.,
RNA-seq ) and basic
machine learning
concepts. Strong problem-solving skills and a desire to learn.
Education and Experience:
MS degree (5+ years of experience) or PhD (0+ years of experience) in a
quantitative field
( Bioinformatics, Computational Biology, Computer Science, Computational Biology , or related a field).
About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com.
US Tech Solutions is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.