Manhattan WMOS Engineer
ShyftLabs
Support Manhattan WMOS implementations and operations as a WMS Engineer at ShyftLabs. ShyftLabs builds data products and digital solutions for Fortune 500 companies.
Deschide
Syneos Health este o organizație complet integrată de soluții biofarmaceutice, care ajută clienții să ducă terapiile de la dezvoltarea clinică până la furnizarea comercială. Echipele sale combină expertiza clinică, afacerile medicale, activitățile comerciale, tehnologia și informațiile despre piață pentru a răspunde nevoilor în schimbare din domeniul sănătății și pentru a sprijini dezvoltarea și lansarea unor tratamente importante în întreaga lume.
Senior Biostatistician – Biomarkers, AI and Deep Learning
Lead biomarker and genomics analyses using advanced statistics and deep learning to support clinical research and life sciences drug development at Syneos Health.
Descriere
- Coordinate cross-functional planning, execution, and analysis of genomics and other biomarker components in clinical and biomarker studies.
- Act as the project biomarker statistician with guidance from an experienced statistician.
- Create Biomarker Evaluation Plans for assigned studies.
- Contribute statistical expertise to clinical development discussions and apply AI and machine learning methods.
- Coordinate statistical workstreams supporting biomarker research and development.
- Plan and perform advanced genomics and biomarker data analyses.
- Write statistical programs and develop complex analytical algorithms.
- Analyze RNA, Olink, and other biomarker datasets.
- Prepare accurate Biomarker Evaluation Reports and explain their findings.
- Resolve routine and complex statistical questions, modeling needs, and analytical tasks independently.
- Create and apply standardized processes for routine biomarker evaluations.
Cerințe
- PhD or MSc in Biostatistics, Statistics, Mathematics, or a related discipline.
- Strong written, verbal, and interpersonal communication abilities.
- Able to work effectively both independently and collaboratively.
- Capable of working with minimal supervision.
- Demonstrated experience developing and deploying deep learning models with PyTorch, TensorFlow, or Keras.
- Strong grasp of machine learning algorithms and statistical methods, including survival analysis and batch-effect management, applied to pharmaceutical or biomarker discovery work.
- Familiarity with explainable AI methods such as SHAP, attention attribution, and counterfactual analysis.
- Understanding of uncertainty quantification for biomarker interpretability.
- Experience independently leading a biomarker study.
- Prior clinical trial experience is preferred.
- Industry experience is preferred.
- Strong understanding of biomarker evaluation, particularly in immunology and infections, type 1 diabetes, relevant parameters, and associated biology.
- Proficiency with statistical programming tools including R, Bioconductor, genetic-data R packages, and Python.
- Experience working with high-dimensional datasets.
- Comparable experience, skills, or education may be accepted.
Beneficii
- Opportunities for career development and progression.
- Supportive, engaged line management.
- Training in technical and therapeutic areas.
- Peer recognition programs.
- Total rewards program.
- Inclusive workplace culture.
- Reasonable accommodations where appropriate.
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