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Proficiency in AI/Gen AI tools and techniques (e.g., machine learning, natural language processing). Experience with data visualization tools (e.g., Tableau, Power BI, etc.
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Provide technical expertise and guidance in AI/ML algorithms, data modeling, data engineering, and data governance and support the deployment across various domains (e.g., natural language processing, computer vision, predictive analytics.
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Machine Learning: Proficient in machine learning techniques, including supervised and unsupervised learning, deep learning, computer vision, and natural language processing.
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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Demonstrates advanced functional knowledge of data visualization libraries such as matplotlib or ggplot2; knowledge of other visualization tools such as Microsoft Power BI and Tableau. Advanced knowledge of advanced techniques such as: dimension reduction techniques, natural language processing, sentiment analysis, anomaly detection, geospatial analytics, etc.
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Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for Compliance-Complaints.
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Understanding of data models, large datasets, business/technical requirements, BI tools, statistical programming languages and libraries. Significant experience in using two or more of the following modeling types to solve business problems: classification, regression, time series, clustering, text analytics, survival, association, optimization, reinforcement learning.
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Using natural language processing (NLP), machine learning (ML), Generative AI, and other relevant AI technologies and platforms; Understanding or hands on experience with Lex, Nuance Mix, Dialog Flow, AWS and Google Cloud, R, Power BI, Tableau, Alteryx.
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Developing machine learning, data mining, statistical network, natural language processing, text analytics, and graph-based algorithms to analyze massive data sets. Experience creating meaningful data visualizations and interactive dashboards using platforms such as Tableau, Qlik, Power BI, RShiny, plotly, and d3.
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Additional preferred qualifications include working with unstructured text and natural language processing. Developing and implementing statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics as well as to develop statistical tests to make data-driven recommendations and decisions.
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Unstructured text and natural language processing. Experience working with tools, including object-oriented programming (Python, Java), computational analysis tools (R, MATLAB), and associated data science libraries (scikit-learn.
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Familiarity with Natural Language Processing, Machine Learning, Predictive modeling, Statistical Analysis and Hypothesis testing. Is familiar with disciplines such as Natural Language Processing, Machine Learning, Predictive modeling, Statistical Analysis and Hypothesis testing.
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Experience with machine learning frameworks and libraries (e.g., scikit-kearn, TensorFlow, PyTorch) and data visualization tools such as Tableau, Power BI, matplotlib, ggplot, Periscope, Business Objects, D3, SAS Visual Analytics or similar platforms.
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Data Visualization: Proficiency with data visualization tools such as Tableau, Power BI, or similar. Experience: Minimum of 6 years of experience as a Data Scientist or in a similar role, with a strong background in developing and implementing machine learning models.
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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure Client (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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