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Omer AcarScientific data systems for biotech teamsBook a discovery call

Scientific data systems for biotech teams

Turn complex biotech data into systems your teams can use.

I help biotech and pharmaceutical teams replace fragmented data and manual scientific workflows with validated pipelines, practical dashboards, and reliable research data systems.

Book a discovery callSend a project brief

Selected project and research outcomes

Sarepta Therapeuticsof research and clinical data supported
5+ TB
Sarepta Therapeuticsprocessing-time reduction through automated pipelines
75%
University of Pittsburghof sequencing and experimental data
10+ TB

Where I can help

Built for teams where scientific data has outgrown ad hoc workflows

A focused consulting engagement can help when data work is slowing scientific progress.

  • Scientific data is split across files, APIs, instruments, or data platforms.
  • Manual processing slows repeat analyses or makes results hard to reproduce.
  • Scientists need a practical way to explore data without depending on one-off scripts.
  • Important experimental information remains trapped in documents or spreadsheets.

Services

What I can help your team build

Focused technical delivery for scientific data workflows, applications, and document processing.

Scientific Data Workflows

Validated pipelines that turn fragmented scientific inputs into reliable, analysis-ready data.

Useful when: Manual processing is slow, hard to reproduce, or difficult to monitor.

  • Ingestion from lab outputs, files, LIMS, APIs, or cloud storage
  • Transformation, validation, harmonization, and orchestration
  • Implementation with Python, Dagster, AWS Lambda, or dbt

Scientist-Facing Applications

Practical tools for exploring genomic, clinical, and research data.

Useful when: Scientists need reliable access without depending on one-off scripts.

  • R Shiny or Streamlit interfaces tailored to scientific teams
  • Integration with Snowflake, Amazon S3, or Parquet-based pipelines
  • Reproducible views for scientific data exploration

Scientific Document Processing

Validated extraction of structured scientific data from unstructured sources.

Useful when: Important experimental information remains trapped in documents or spreadsheets.

  • Experiment metadata extraction from PDF, Word, or plaintext sources
  • Curated outputs for loading into scientific data platforms
  • AI-assisted scientific text analysis with deliberate validation

Evidence

Evidence from scientific data work

Named experience connecting scientific requirements with production-grade data engineering.

Sarepta Therapeutics

of research and clinical data supported
5+ TB
processing-time reduction through automated pipelines
75%

Bioinformatics consulting and data engineering for production genomics and clinical data platforms.

  • Built systems supporting variant annotation, omics integration, and downstream analysis across 5+ TB of research and clinical data.
  • Developed scalable ingestion, validation, harmonization, and analysis pipelines that reduced processing time by 75%.
  • Collaborated with computational biology, bioinformatics, clinical, immunology, and engineering teams.

University of Pittsburgh

of sequencing and experimental data
10+ TB

Graduate research in computational biology and reproducible multi-omics analysis.

  • Designed genomics and multi-omics workflows using Python, R, SQL, and Snakemake for 10+ TB of sequencing and experimental data.
  • Collaborated with interdisciplinary research teams and co-authored peer-reviewed publications.

How it starts

How an engagement starts

A practical path from an unclear data problem to a useful delivery.

  1. Understand the constraint

    Discuss the scientific question, current workflow, users, and operational constraints.

  2. Define the smallest useful delivery

    Agree on scope, working approach, and what a useful handoff looks like.

  3. Build with the team

    Deliver iteratively with validation, documentation, and project-appropriate knowledge transfer.

Working together

Flexible by design

Choose the level of support that fits the problem, timeline, and team.

Project-based consulting

A defined delivery when one workflow, tool, or data challenge needs focused ownership.

Part-time consulting

Ongoing support when priorities shift and the team needs reliable technical capacity.

Embedded collaboration

Close collaboration when delivery depends on regular work with scientific and data teams.

Scientific credibility

Research foundation

PhD in Computational Biology from Carnegie Mellon University and the University of Pittsburgh, with peer-reviewed work spanning genomics, evolution, and computational biology.

View Google Scholar
  1. Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome2024
  2. A vast evolutionarily transient translatome contributes to phenotype and fitness2023
  3. De novo emergence of adaptive membrane proteins from thymine-rich genomic sequences2020

Data and analysis

  • PythonPipelines, automation, and data modeling
  • RStatistical analysis and genomics with Bioconductor
  • SQLQuerying clinical and omics datasets
  • AWS BedrockFoundation models for scientific text analysis and data extraction workflows
  • Document processingStructured data extraction from scientific literature and reports

Applications

  • ShinyDashboards for genomics and clinical data exploration
  • StreamlitPython-based data science applications
  • React and Next.jsInteractive web applications and dashboards

Infrastructure

  • AWSCloud compute, storage, and bioinformatics infrastructure
  • SnowflakeStorage and querying for clinical and omics data
  • DagsterReliable and observable data pipelines
  • Docker and KubernetesPortable tools and scalable workflows
Omer Acar

About

About Omer

I’m a data engineer and computational biologist with a PhD in computational biology. I build systems that help scientific teams work with complex genomic, clinical, and research data.

My work spans validated data workflows, scientist-facing applications, bioinformatics deployments, and AI-assisted extraction where scientific accuracy still requires deliberate review.

GitHubLinkedInEmailGoogle Scholar

Have a scientific data challenge in mind?

Turn the next data bottleneck into a system your team can use.

Book a discovery callSend a project brief