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Job Schedulers – Airflow, Oozie or other ETL Scheduler Data Analytics – AWS Cloud Native Services Big data architecture – Python, Scala, Hadoop Bigdata, Hive, Pyspark etc. Data bases (SQL, NoSQL) and Data Platforms (Databricks, Snowflake) Strong skills with Database management, Data modeling and design DevOps and Security CI/CD, Serverless compute, OAuth, SSO, MFA etc.
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Use Microservices, NoSQL, Spring Boot, RESTful services, Cloud technologies, AWS, Java, Junit, Spring, Karate, Cucumber, Gherkin, Spring Security, Hibernate, Kafka, Angular, MVC, Jenkins, Git, BitBucket, CI/CD, HTML, CSS, SQL, Python, TCP, IP, JMS, IntelliJ, Giffy, Visio, Gradle, Maven, Postman, Jupiter, Selenium.
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Python, Ansys HFSS.Computational Fluid Dynamics (CFD). Python, Ansys HFSS.Computational Fluid Dynamics (CFD). Experience with any of the following technology areas is highly desired: C/C, SiC, quartz, phenolic, and high temperature materials.
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Lead the creation of the data strategy, data governance, and associated practices in collaboration with the CIO. Extensive knowledge and experience in data engineering, data analytics, data governance, AI, and data transformation.
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Knoledge in Databricks, PowerBI, programing languages including SQL, Python and PySpark, data principles including data pipeline, streaming data, data storage, ETL, data integration, database design Leadership (Vision, strategy and business alignment, people management, communication, influencing others, managing change.
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Demonstrated proficiency with data science languages (R, Python, SQL, Julia, SAS) and visualization tools (Tableau, Spotfire, SAS, JMP, RShiny) The Clinical Data Analyst partners cross functionally to identify risk to data integrity throughout the data flow to drive quality collection for non-EDC data sources (fluid & tissue labs, digital, imaging, wearables) in Dyne's clinical studies.
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We are a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and DevOps, data / metadata / knowledge platforms, and AI/ML and analysis platforms, all geared toward:Building a next-generation data experience for GSK’s scientists, engineers, and decision-makers, increasing productivity, and reducing time spent on “data mechanics.
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Proven experience (5+ years) in software engineering, with expertise in Python, Airflow, Snowflake, and AWS. Strong leadership and managerial skills, with a track record of successfully leading and mentoring engineering teams.
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Proficient in integrating experimental data into Laboratory Information Management Systems (LIMS) to ensure accurate and efficient data management and accessibility is preferred. Programming experience with Python and/or R (R Studio) is a plus.
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Provides ad-hoc programming support to Clinical Operations, Data Management, Statistical Programming, Biostatistics, Drug Safety, Clinical Science, Medical Writing and Drug Safety to ensure rapid access to clinical study data for data cleaning/validation, SAS listings review, TLF, dry runs, etc.
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Equal Opportunity Employer/Veterans/DisabledBy applying, you consent to your information being transmitted by Jobcase to the Employer, as data controller, through the Employer’s data processor SonicJobs.
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Experience using relational databases (e.g. MS SQL Server, MS Access, or Oracle) and data visualization tools (e.g. Spotfire, jReview) Serve as Clinical Data Scientist and Trial Lead for one or more clinical trials assuming responsibility for all CDS activities including selection and application of data acquisition standards, Data Management Plan, selection of quality risk indicators, third party study data due diligence.
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Demonstrated ability in Python (or other scientific programming) to curate and analyze data at scale and to implement new metrics to predict and select peptide designs. The successful candidate should have a high degree of proficiency in Python (or other scientific programming), machine learning libraries, Cheminformatics tools, and cloud computing and be responsible for developing complex workflows to support the selection, design and optimization of peptide modalities and formulations.
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Understands objectives; safety, efficacy, statistical endpoints; epidemiological and operational elements; the biology, pharmacology and toxicology; data driven phenotypes identification, biomarkers, microbiology, virology.
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Experience with state-of-the-art AI & ML methods applied to biological sequence data (DNA, protein)Hands-on experience with public omics, protein and immune repertoire datasets such as PDB, OAS, SAbDab. Strong experience with scientific programming languages (Python, R) and data science libraries.
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