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Computational Spatial Transcriptomics Jobs in Maryland

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Computational Spatial Transcriptomics information

What are some typical challenges faced when working in computational spatial transcriptomics, and how can new team members prepare for them?

Professionals in computational spatial transcriptomics often encounter challenges related to handling and analyzing large, complex datasets that combine spatial and gene expression information. Integrating data from different technologies and ensuring data quality can be demanding, requiring strong programming skills and familiarity with bioinformatics pipelines. New team members can prepare by strengthening their skills in statistical analysis, programming languages like Python or R, and staying updated on the latest spatial transcriptomics techniques. Collaborating closely with experimental biologists and data scientists is also key to overcoming these challenges and driving successful research outcomes.

What is the difference between Computational Spatial Transcriptomics vs Computational Biologist?

AspectComputational Spatial TranscriptomicsComputational Biologist
Required CredentialsAdvanced degrees in bioinformatics, computational biology, or related fields; experience with spatial data analysisTypically a PhD or Master's in biology, bioinformatics, or related disciplines; strong programming skills
Work EnvironmentResearch labs, biotech companies, academic institutions focusing on spatial genomicsResearch institutions, biotech firms, academia working on biological data analysis
Industry UsageSpecialized in spatial transcriptomics techniques and data interpretationBroad biological data analysis across various fields

Computational Spatial Transcriptomics focuses on analyzing spatial gene expression data within tissues, requiring specialized skills in spatial data processing. In contrast, Computational Biologists work on a wider range of biological data types. While both roles involve bioinformatics expertise, the former emphasizes spatial data analysis techniques specific to transcriptomics.

What is computational spatial transcriptomics?

Computational spatial transcriptomics is a field that combines advanced computational methods with spatial transcriptomics, a technique that measures gene expression within the physical context of tissue samples. It involves processing and analyzing large datasets to map where specific genes are active within tissues, helping researchers understand how cells interact and function in their native environments. This approach is crucial for studies in developmental biology, cancer research, and neuroscience, as it provides insights into cellular organization and tissue architecture. Computational tools help extract meaningful patterns from complex data, enabling discoveries that were previously impossible with traditional methods.

What are the key skills and qualifications needed to thrive as a Computational Spatial Transcriptomics Scientist, and why are they important?

To excel in Computational Spatial Transcriptomics, you need a strong background in bioinformatics, genomics, and statistical data analysis, typically supported by advanced degrees in computational biology or related fields. Familiarity with programming languages (such as R and Python), spatial transcriptomics platforms (like 10x Genomics Visium), and high-throughput sequencing data analysis tools is essential. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex datasets and collaborating with multidisciplinary teams. These competencies ensure accurate data interpretation, innovative research, and successful integration of spatial transcriptomics insights into biological and clinical applications.
What are popular job titles related to Computational Spatial Transcriptomics jobs in Maryland? For Computational Spatial Transcriptomics jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching Computational Spatial Transcriptomics jobs in Maryland look for? The top searched job categories for Computational Spatial Transcriptomics jobs in Maryland are:
What cities in Maryland are hiring for Computational Spatial Transcriptomics jobs? Cities in Maryland with the most Computational Spatial Transcriptomics job openings:
Computational Spatial Transcriptomics jobs near you
Data Scientist (SOM Pediatric Neonatology)

Data Scientist (SOM Pediatric Neonatology)

Johns Hopkins University

Baltimore, MD • On-site

$99K - $175K/yr

Full-time

Posted 7 days ago

Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 202 frontline employees who took The Breakroom Quiz

227th of 873 rated healthcare providers

Job description

The School of Medicine Pediatric Neonatology is seeking a Data Scientist to support the management of complex databases as well as develop, maintain, and document code. The Data Scientist will collect and extract data using a variety of data extraction tools and carry out data management, visualization, and analysis tasks.
Specific Duties & Responsibilities
  • Support the development of dashboards, calculations, and reports which may require programming.
  • Support the development and tuning of machine learning models, which may require programming.
  • Support the development of infrastructure for cleaning and processing data and running experiments to evaluate system performance.
  • Mine various data resources for development of key metrics, performance indicators, and general business intelligence useful for planning and service design.
  • Perform error analysis and suggest and implement improvements.
  • Other duties as assigned.

Minimum Qualifications
  • Bachelor's Degree.
  • Four years of relevant quantitative research and analytics experience, with at least two of those years including complex programming experience.
  • Additional education may substitute for required experience and additional related experience may substitute for required education permitted by the JHU equivalency formula. Beyond a high school diploma/graduation equivalent, to the extent

Preferred Qualifications
  • Bulk RNA-seq, single-cell transcriptomics, spatial transcriptomics, metabolomics, proteomics, and microbiome data
  • Proficiency in R and/or Python
  • Machine learning (supervised and unsupervised) and deep learning.
  • Adept in developmental environments including: RStudio, VS code, Jupyter.
  • Experience with tools such as Seurat, Scanpy, DESeq2, edgeR, Monocle, CellRanger, QIIME2, HUMAnN, or comparable platforms
  • Familiarity with Snakemake, Nextflow, or similar workflow frameworks preferred
  • Experience in medical imaging, multi-omics, and clinical data integration with the ability to deliver reproducible and efficient workflows.
  • Proficient in Python, R, SQL, Linux, and deep learning frameworks, with a strong focus on applying computational methods to solve real-world biomedical problems

Technical Skills & Expected Level of Proficiency
  • Continuous Improvement - Developing
  • Data Architecture - Developing
  • Data Management and Analysis - Developing
  • Data Tools and Platforms - Developing
  • Data Validation and Quality Assurance - Developing
  • Data Visualization - Developing
  • Machine Learning - Developing
  • Oral and written communications - Developing
  • Programming Languages - Developing

The core technical skills listed are most essential; additional technical skills may be required based on specific division or department needs.
Classified Title: Data Scientist I
Job Posting Title (Working Title): Data Scientist (SOM Pediatric Neonatology)
Role/Level/Range: ATP/04/PG
Starting Salary Range: $99,800 - $175,000 Annually ($99,000 targeted; Commensurate w/exp.)
Employee group: Full Time
Schedule: M-F; 8:30 to 5pm (evenings/weekends will be required)
FLSA Status: Exempt
Location: Hybrid/School of Medicine Campus
Department name: SOM Ped NICU
Personnel area: School of Medicine

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