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Be and expert in Python, SQL, machine learning algorithms, and know about data integration, modeling and be well-versed in versed in good UI design. Programming Skills - Expertise in statistical programming languages like Python, database query languages like SQL, and machine learning packages like scikit-learn, pandas, xgboost etc.
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Master’s degree in Data Science, Bioinformatics, Computational Biology, Machine Learning, Statistics, Mathematics, Physics, and 3+ years of professional experience. Demonstrated expertise in core coding environments including Python, R, SQL, bash scripts.
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Proficiency in programming languages such as Python, R, or SQL. Strong knowledge of machine learning techniques, statistical analysis, and data visualization tools Tableau, Power BI.
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Practical working experience in utilizing and operating cloud services (preferably Azure); Snowflake, python, SQL, Dataiku, Databricks, Machine Learning, etc. Process Management: Applies continuous improvement processes to develop, implement and sustain analytics platforms via use of CI tools and leveraging analytics technologies (Snowflake, Python, SQL, Dataiku, Databricks, Azure, etc.
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Experience with data science, statistical analysis, data mining, and predictive algorithms to support advanced/predictive analytics. Experience in Data Engineering tools such as Talend, SAP BODS, Python, PySpark, AWS S3, Glue, Athena, EMR, Kafka or similar tools.
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Exposure to Data Lake, Lake House concepts, and technologies like Snowflake, AWS RedShift, or Databricks. Support GRC (Governance, Risk, Compliance) aspects of data management. Experience managing large complex Data Engineering projects and teams across different time zones.
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2+ years of experience with one or more of the following tools: Data Gathering & Analysis skills/tools (e.g. SAS, Python, SQL, BigQuery, Vertex AI, Business Objects, Alteryx, etc.
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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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Manipulates and extracts Medicare, Medicaid, and other healthcare claims data stored in Cloud environment using appropriate software such as SAS, Snowflake, Python, R, SQL, and other software as appropriate for the task.
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Advanced and hands on experience using: Python, Databricks, Azure ML, Azure Cognitive Service, Ads Data Hub, BIQuery, SAS, R, SQL, PySpark, Numpy, Pandas, Scikit Learn, TensorFlow, PyTorch, AutoTS, Prophet, NLTK.
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Proficient in Spotfire/Tableau, SQL, Python, R as well as ability to develop advanced visualizations via R-shiny, Dash, or JavaScript technologies. Regularly apply theories and principles from Data Science to develop and deploy asset performance monitoring tools.
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Java, Python, ScalaExperience in Natural Language Understanding, Computer Vision, Machine Learning, Algorithmic Foundations of Optimization, Data Mining or Machine Intelligence (Artificial Intelligence.
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Machine Learning | Infrastructure | AI | Python | SQL | Software Engineering| Deep Learning | Pytorch | Tensorflow. Reporting directly to the Director of Data Science, you will collaborate closely with cross-functional teams.
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PhD or other Doctoral Degree in cognitive science, educational psychology, education, educational data mining, or equivalent. Knowledge of R Markdown, LateX, Qualtrics/Survey tools, Mechanical Turk, GitHub, SQL and/or R Shiny.
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Knowledge of Statistics, Gen AI, LLM, Python, Computer Vision, data visualization tools. Required Skills for Data Science/Machine Learning Positions Bachelor's degree or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Information Systems, IT.
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data science mining python sql jobs Company: Clearancejobs in IN, Pennsylvania
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When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
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In the current candidate-driven job market, recruiters are looking for unique ways to attract talent. Some have resorted to even (dare we say it?) recruitment strategies on the border of weird and wacky. What can we learn from the unusual recruitment tactics that are being used and actually getting results? Here’s a rundown of some unique recruitment strategies that actually work.
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If you’re like most of us, you’d love to be wealthier. Having more money would alleviate stress. It would make it easier to pay your bills and buy nicer things. Maybe it’d allow you to spend more time with your kids and go on more vacations. You’re not alone if you wish you could somehow earn a more significant income.