Principal Bioinformatics Data Scientist

QUALIFICATIONS:

• Education:

• Master’s and higher degree (PhD preferred) in Bioinformatics, Computer Science, Data Science, Computational Biology, or a related field.

• Experience:
• 6+ years of professional experience in genomic data science related to bioinformatics, computational genomics, or similar, including at least 3 years in a senior or lead role.
• Proven track record of statistical, machine learning, and AI model development using genomic and clinical data.
• Strong experience with secondary and tertiary genomic analysis (alignment, variant calling, annotation, and interpretation).
• Experience in data Lakehouse (Databricks, Snowflakes), and precision health platforms (DNAnexus, Velsera). 
• Experience in big data, data ETL, data visualization, workflow orchestration/logging, and databases (including SQL, no-SQL, and graph-based).
• Demonstrated experience working in a clinical or diagnostic genetics environment is highly desirable.
• Proficient in Python, R, C/C++, Java, or similar programming languages.
• Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, XGBoost).
• Advanced understanding of statistical modeling, including Bayesian inference, GLMs, mixed models, and resampling methods.
• Experience applying deep learning architectures (transformers, CNNs, GNNs) to genomic and biomedical data.
• Deep understanding of statistical analysis, data modeling, and computational methods used in genomics.
• Experience with NGS data formats and genome databases.
• Familiarity with cloud computing environments (Azure, AWS, GCP) and distributed computing frameworks (e.g., Spark, Dask).
• Deep knowledge of statistical modeling, dimensionality reduction, and data visualization.
• Familiarity with CI/CD, containerization (Docker/Kubernetes), and version control (Git).
• Core Competencies:
• Exceptional analytical, problem-solving, and critical thinking skills.
• Ability to translate complex data-driven analyses into actionable biological and clinical insights.
• Excellent written and verbal communication skills, with the ability to communicate effectively across disciplines.
• Deep understanding of both computational methods and biological context.
• Demonstrated leadership in cross-functional team environments.
• Passion for innovation in precision medicine and clinical genomics.

DUTIES AND RESPONSIBILITIES:

• Algorithms and Model Development:

• Design, develop, and optimize computational algorithms, statistical models, and machine learning/AI approaches for genomic data analysis.
• Develop algorithms for variant prioritization, pathogenicity prediction, phenotype-genotype association, and diagnostic decision support.
• Lead efforts to apply deep learning and predictive modeling to variant interpretation, phenotype correlation, and diagnostic decision support.
• Develop and optimize algorithms for secondary and tertiary genomic analysis, variant calling, variant annotation, ACMG classification, and data-driven interpretation of genomic findings.
• Apply deep learning architectures (CNNs, RNNs, GNNs, transformers) and probabilistic modeling approaches to improve variant calling, variant interpretation, and disease prediction.
• Design and optimize Bayesian, regression, and ensemble models to quantify uncertainty, improve confidence scoring, and support clinical decision-making.

• Develop feature engineering and dimensionality reduction strategies for multi-modal data integration (genomic, transcriptomic, phenotypic, and clinical).

• Genomic Analysis:
• Serve as a subject matter expert (SME) in data science applications for bioinformatics pipeline development, including secondary and tertiary analysis, variant interpretation, and clinical reporting automation.
• Drive enhancements to existing bioinformatics pipelines for improved accuracy, performance, and interpretability.
• Integrate and analyze multi-omics datasets (genomic, transcriptomic, phenotypic, and clinical) to extract meaningful biological and clinical insights.
• System Innovation and Optimization:
• Evaluate and enhance existing bioinformatics and data science pipelines for improved accuracy, speed, and scalability.
• Collaborate with engineering teams to integrate new algorithms and frameworks into production-grade analysis pipelines.
• Drive novel genomic data platform development to support a cohesive data ecosystem.
• Research and Development:
• Drive innovation in genomics data science through research and development of novel analytical methodologies.
• Stay current with emerging tools, frameworks, and technologies in ML, AI, bioinformatics, and genomics, and guide the team in adopting best practices.
• Cross Functional Collaboration:
• Partner closely with laboratory scientists, clinical geneticists, software engineers, and data engineers to translate scientific insights into clinical applications.
• Serve as a scientific and technical thought leader across cross-disciplinary projects.
• Evaluate emerging technologies, frameworks, and methodologies to ensure Baylor Genetics remains at the forefront of computational genomics innovation.
• Present findings and strategic recommendations to executive and scientific leadership.
• Mentorship:
• Provide technical mentorship and guidance to bioinformatics scientists, data scientists, and software engineers.
• Contribute to strategic planning and technical direction for the Bioinformatics R&D and Data Science group.

WHY JOIN BAYLOR GENETICS

PHYSICAL DEMANDS AND WORK ENVIRONMENT:

• Frequently required to sit
• Frequently required to stand
• Frequently required to utilize hand and finger dexterity
• Frequently required to talk or hear
• Frequently required to utilize visual acuity to operate equipment, read technical information, and/or use a keyboard
• Occasionally exposed to bloodborne and airborne pathogens or infectious materials

EEO Statement: