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Creates database-to-deployment pipelines for models using the necessary programming languages (primarily R, Python, SQL) and tools (AWS SageMaker, Dataiku, Databricks etc. Experience with DataBricks, Dataiku AWS SageMaker, Azure, and data pipeline management tools like Airflow.
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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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What you will be responsible forDevelop/ Build and enhance data pipelines using Python, Spark based data engineering solutions (Databricks), and SQL (AWS Redshift). Hands on development in Python, PL/SQL, SQL, Shell Scripting, AutoSys. Hands on experience working in cloud data platforms such as AWS Redshift, Spark based data engineering solutions.
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Proficiency in data visualization tools (, Tableau, Salesforce CRMA), SQL and SAQL, and statistical analysis software (, R, Python). The ideal candidate will have strong analytical skills, extensive experience with data visualization tools such as Salesforce CRMA and Tableau, and a deep understanding of fundraising metrics and processes.
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Full stack software engineering experience, with proven experience in data visualization modules or frameworks like Python Dash and R Shiny. Solid software development skills including experience in R, Python and SQL. Knowledge of Database design and query optimization a plus.
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The Onyx Research Data Platform organization represents a major investment by GSK R&D and Digital & Tech, designed to deliver a step- change in our ability to leverage data, knowledge, and prediction to find new medicines.
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Big Data & Streaming: 2+ years using big data and/or streaming technologies (e.g. Apache Spark, Apache Kafka, Apache Flink) Data: Good understanding of data & data processing tools (e.g. Spark, Kafka, SQL), of relational database technologies and of analytics databases (e.g. Redshift, Vertica, Snowflake.
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Experience with data visualization and web development software including but not limited to Tableau and PowerBI.Experience with web development platforms and coding applications such as python, JavaScript, Java, or R. Experience in communications and/or graphic design a plus.
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As a Senior Economist, you will use open-source programming languages (R or Python) in Databricks to analyze large, complex, but well-structured data sets from our data warehouse (SAS is also available if desired.
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Good/Advanced programming skills in Matlab/Python/R. LabView and C. 4+ years field experience in oil & gas, geothermal, hydrogen, CO2 and/or mining applications of geochemical data.
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Strong programming skills in Python, R, or other relevant languages. Data Science Management, Director Data Science, Director of Data Science, Director of Engineering, Senior Director of Data Science, Senior Director Engineering, Manager Data Science, Manager Engineering, Artificial Intelligence Management.
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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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We’re helping patients get the best care possible by working on digital health experience, analyzing big data to streamline the delivery of care, and reaching out to medically complex patients to better understand their needs.
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R, SQL, Matlab, Python, Java, JavaScript, and HTML/CSS. Develops software applications using R/Shiny or Web UI development. DE building scalable quantitative software platforms to enable Computational Finance Experimentation, Backtesting, Risk Management, Performance Attribution, Monitoring, and Evaluation of Financial Investment Strategies, using Object Oriented Programming, python, R, and MATLAB in Linux and Windows environments.
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Demonstrated Experience in Python programming and familiarity with core data science libraries. Deliver datasets from onboarding through mapping and automated DQ so they can be handed off to the Data Operations team for DQ maintenance and consumed by research teams and production.
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