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Demonstrated experience with the Python, R, Scala. Use programming languages like Python, Scala, or R. Demonstrated professional or academic experience developing models and ensembles in the AI/ML space, including selecting the best Python libraries for a given task, choosing appropriate pre-processing actions, performing analysis, and assessing model performance.
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Experience with analytics tools like SAS, R, Python, and other advanced statistical software. Experience with at least one of the programming languages such as: JavaScript, Java, Python, or other.
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Will maintain, move, and manipulate data between applications, using appropriate software/code: Apache Spark, ElasticSearch, R, Python, Kibana and others as technology evolves. Experience utilizing computer programs, software, or a variety of coding languages (i.e., Python, MySQL, D3, SPSS, SAS, Visual Basic, R, etc.
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Experience with programming languages used for scientific data analysis, including, for example, Python, R, MatLab. PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics.
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Must have extensive experience writing scripts in Visual Basic, R, Python or other software for statistical models using large datasets to extract patterns, relationships, and anticipatory behaviors that are repeatable, efficient, hypothesis testing, and knowledge capture.
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Data science tools/languages: R, R Studio, Python (data preparation and analysis libraries), Databricks, etc. Object-oriented/scripting languages: Python, Java, C. Data science tools/languages: R, R Studio, Python (data preparation and analysis libraries), Databricks, etc.
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Ability to understand and automate repeated tasks using Python, R, SQL language, and/or Arcade expressions. Ability to understand and automate repeated tasks using Python, R, SQL language, and/or Arcade expressions.
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Programming skills with SQL, Excel VBA, R and Python preferred. As the Hedging Analytics Manager, you are the heartbeat of our Mortgage Servicing Rights (MSR) and Fair Value Mortgage portfolio (FVMP) hedge strategy.
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Strong programming experience in Java and Python/R. Familiarity with SAS and with tools available in the AWS/Cloud environment. Develop, implement and test all components using Java/Python/R of financial models, algorithms, cash flow simulations and pricing/risk metrics calculations in end-user or production computing systems for use in business decisions, financial and regulatory reporting, and risk management.
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AWS cloud services such as EC2, EMR, RDS, Redshift, Glue, SageMaker (or Azure and GCP equivalents) Our data exploitation approach is tightly integrated with Human-Centered Design and DevSecOps. Experience in crafting and implementing data lakehouse solutions in the cloud (Preferably AWS. Alternatively, Azure, GCP.
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Analytics applications (Such as R, Python, SAS, STATA, MATLAB, etc.) 2+ years of experience in Python and cloud-based SDKs for data manipulation, scripting, and automation. Hands-on experience with Databricks or Snowflake.
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Experience in implementing statistical tools and machine learning algorithms and libraries like Python, Matlab, R, SAS, Stata, etc. Candidate must have experience with Databricks Informatica and Snowflake.
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Develop, implement and test components using Java/Python/R of financial models, algorithms, cash flow simulations and pricing/risk metrics calculations in end-user or production computing systems for use in business decisions, financial and regulatory reporting and risk management.
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Practical experience in a wide range of bioinformatics software Extensive experience with R, Python, Bash. Courses and workshops may include but are not limited to the following: basic, intermediate, and advanced R and Python programming and Bash shell scripting; bulk RNseq analysis; scRNseq analysis, ChIP-seq analysis, tooling such as RMarkdown, Jupyter notebooks, git, GitHub, Snakemake, Slurm; and/or concepts such as alignment, clustering, linear modeling, experimental design, visualization.
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Develop scientific applications using R, Shiny and/or Python. Knowledge of technology tools and platforms such as MATLAB, R languages, PostgreSQL, GitHub, Linux, Python or Shiny.
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