What are the responsibilities and job description for the Senior Bioinformatics Scientist (South San Francisco, CA) position at CEDENT?
We are seeking a highly motivated and innovative Senior/Staff Bioinformatics Scientist to join our R&D team focused on synthetic biology product development. In this role, you will work at the intersection of computational and molecular biology, developing advanced DNA sequence analysis pipelines, gene design algorithms, and predictive models to optimize DNA synthesis and assembly.
You will collaborate with molecular biologists, product teams, and manufacturing engineers to refine client’s core synthetic biology platform, troubleshoot assembly challenges, and improve DNA synthesis efficiency. This position emphasizes predictive modeling, algorithm development, and data-driven approaches to enhance synthetic biology workflows.
Key Responsibilities
Synthetic Biology & DNA Assembly
You will collaborate with molecular biologists, product teams, and manufacturing engineers to refine client’s core synthetic biology platform, troubleshoot assembly challenges, and improve DNA synthesis efficiency. This position emphasizes predictive modeling, algorithm development, and data-driven approaches to enhance synthetic biology workflows.
Key Responsibilities
Synthetic Biology & DNA Assembly
- Develop bioinformatics methods supporting DNA assembly strategies and synthetic biology workflows.
- Apply machine learning and predictive modeling to improve DNA assembly success rates and optimize synthesis parameters.
- Collaborate with wet-lab scientists to analyze and troubleshoot molecular biology processes.
- Develop bioinformatics approaches for diagnosing and resolving complex DNA synthesis and assembly issues.
- Build and maintain NGS analysis pipelines (e.g., Nextflow) to support quality control and performance tracking of synthetic biology products.
- Leverage Oxford Nanopore Technologies (ONT), Illumina, or PacBio sequencing to analyze and optimize synthesis workflows.
- Develop predictive models to identify challenging sequences and recommend optimizations to improve synthesis efficiency.
- Ensure pipeline accuracy, reproducibility, and scalability in research environments.
- Apply advanced statistical and machine learning methods to evaluate improvements in synthetic biology product iterations.
- Use Python, R, and visualization tools to generate actionable insights and present findings to stakeholders.
- Design and implement novel bioinformatics algorithms for sequence analysis and synthetic biology applications.
- Partner with R&D, manufacturing, and product development teams to troubleshoot assay issues and implement data-driven solutions.
- Support the broader Bioinformatics team by integrating synthetic biology workflows with existing genomic data processing pipelines.
- Assist with customer-facing analyses related to synthetic biology assay performance.
- Educational Background & Experience:
- Bachelor’s/Master’s degree with 6–8 years of industry experience, or a Ph.D. with 3 years of industry experience in Bioinformatics, Molecular Biology, Biochemistry, Genetics, Genomics, Computational Biology, or a related field.
- Bioinformatics & NGS Expertise:
- Experience with ONT, Illumina, or PacBio sequencing technologies.
- Proficiency in workflow management tools (Nextflow, Snakemake).
- Hands-on experience designing predictive models for sequence design & synthetic biology applications.
- Technical Skills:
- Expertise in Python (Pandas, NumPy, SciPy) and visualization libraries (Matplotlib, Seaborn, Plotly).
- Experience with machine learning frameworks (scikit-learn, TensorFlow, PyTorch) applied to biological sequence data.
- Familiarity with R, SQL, or other statistical tools.
- Molecular Biology & Synthetic Biology Knowledge:
- Understanding of DNA assembly, cloning techniques, and molecular biology workflows.
- Ability to interpret experimental data and provide feedback on laboratory procedures.
- Experience developing design algorithms for synthetic biology applications.
- Familiarity with deep learning approaches for biological sequence analysis.
- Exposure to high-throughput experimental design and laboratory automation.
- Hands-on experience with gene synthesis and synthetic biology assay development.