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Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, ML and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and ML techniques.
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Your skills include experience with QlikView, Adobe Analytics, Google 360, Python, R Programming, Scala, Apache Spark, and AWS Redshift. Your skills include experience with QlikView, Adobe Analytics, Google 360, Python, R Programming, Scala, Apache Spark, and AWS Redshift.
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Languages: R, Python (Pandas, Seaborn, Scikit-learn), SQL,Hive, Pig, Impala, Sqoop, Kafka, VBA, Shell. Analytics Tools: Excel, Tableau, AWS, BigQuery, NoSQL,Hadoop, Spark, Airflow, Gurobi, JIRA, Stata, Git.
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Snowflake, Informatica, Matillion, BI technologies (PowerBI, Tableau, Qlik, MicroStrategy), SQL, Python, MDM (Reltio Informatica) and Data Governance (Collibra, Informatica)Familiarity with Analytical Workbenches such as R/Python, Alteryx, Dataiku, KNIME, AWS Sagemaker etc.
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Certificate in business analytics, data mining, or statistical analysis, Statistical programming languages (for example, SAS, R) Certificate in business analytics, data mining, or statistical analysis, Statistical programming languages (for example, SAS, R.
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Experience with Quantum Metrics and Akamai, Technologies and languages: Python, Spark, R, etc. Google Data Analytics Professional Certificate, Power BI Data Analyst Associate, AWS Certified Data Analytics, Enterprise Data Warehouse certifications, Palantir, Client Side performance metrics tools e.g.
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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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Experience in Excel, Stata, Python, R, SQL, SAS, ArcGIS/ArcPy, or Cloud computing (Databricks/Spark, Google Cloud, Azure, AWS); Program, build models and/or perform regression analyses in statistical analysis programs (such as Stata, SAS, Python, R, Matlab or Mathematica.
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Data science tools/languages: R, R Studio, Python (data preparation and analysis libraries), Databricks, etc. AWS cloud services such as EC2, EMR, RDS, Redshift, Glue, SageMaker (or Azure and GCP equivalents.
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The candidate should have expertise in data modeling, SQL, Python, R, data warehousing, big data technologies, data pipeline construction, data quality, cleansing, version control, cloud services, machine learning, feature engineering, deep learning, and natural language processing.
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Knowledgeable of technologies such as Scala, Databricks, Python, Trifacta, Data Robot, Kafka, Stream Sets, and/or R programming language. Utilize Scala, Databricks, Python, Trifacta, Data Robot, and R to implement data processing and analysis tasks.
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Experience- Experience with data visualization using Tableau, Quicksight, or similar tools- Experience with data modeling, warehousing and building ETL pipelines- Experience in Statistical Analysis packages such as R, SAS and Matlab- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modelingPREFERRED QUALIFICATIONS- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift- Experience in data mining, ETL, etc.
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Proficiency in data analytics tools and technologies, such as Python, R, SQL, and cloud platforms (e.g., AWS, Azure). Relevant certifications (e.g., Snowflake, Databricks, Certified Analytics Professional, AWS Certified Big Data - Specialty) are a plus.
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Typically, Minimum 4 Years Relevant Exp Demonstrated experience with sophisticated analytics tools and programming languages (e.g., SAS, SPSS, R, Python, SQL, HiveQL, Spark, Hadoop) Proficiency with visualization platforms such as Tableau, Qlikview, PowerBI, D3, or JavaScript.
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Demonstrated experience with sophisticated analytics tools and programming languages (e.g., SAS, SPSS, R, Python, SQL, HiveQL, Spark, Hadoop) Experience with building AWS cloud-native data science applications.
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