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Employees in this role use knowledge of machine learning, optimization, statistics, and applied mathematics along with abilities in software engineering with a focus on distributed computing and data storage infrastructure (e.g. “Big Data.
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Employees in this role use knowledge of machine learning, optimization, statistics, and applied mathematics along with abilities in software engineering with a focus on distributed computing and data storage infrastructure (i.e., “Big Data.
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Avery Dennison is seeking a Materials Scientist to meet organizational needs for accelerated weathering testing, outdoor aging, analytical testing, data analysis/machine learning, and specific weathering research projects with pressure sensitive adhesive film constructions for Graphics & Durables applications.
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Software Engineering & DevOps: Version Control: Familiarity with tools like Git. Continuous Integration/Continuous Deployment: Knowledge of pipelines and tools like Jenkins, Travis CI. Containerization: Familiarity with Docker, Kubernetes, etc., for scalable deployment of data science applications.
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Techniques: Statistical Analysis, Visualization, Optimization, Machine Learning, Big Data, Data Warehouse, NLP. Drive the adoption of data science-driven mechanisms and machine learning models to continuously evaluate and improve catalog data quality.
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Certificate in business analytics, data mining, or statistical analysis, Statistical programming languages (for example, SAS, R) Advanced Analytics - Applied Econometrics, Advanced Calculus, Statistical Methods, Data Visualization, Decision Analytics & Optimization, ML, Big Data, Social Network Analytics.
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Define business priorities across channels, 1P and Marketplace, collaborating with cross functional teams (engineering, product, data science, and operations), ensuring there is a strong roadmap.
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Enhance/Develop/Sustain current decision support tools or replacements along with internal and external portals using data/ tools such as Army VANTAGE, ADVANA, Microsoft Power Automate, data visualization tools (Tableau, Qlik, PowerBI), Python with data libraries of (PySpark, Pandas, NumPy), Structured Query Language (SQL), Statistical Analysis System (SAS), Oracle, Microsoft Azure, Microsoft Suite of products, machine learning methods, and other data analytics tools, as necessary.
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With tech innovation an integral part of our core business, the booming city proved an ideal backdrop for our heavy investment into machine learning, research infrastructure and big data computing.
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7+ years of direct sales experience carrying and exceeding a quota and selling enterprise software/ software-as-a-service (Business Intelligence/ Data Warehouse/ Big Data/ Advanced Analytics/ Machine Learning) to C-level executives.
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The Generative Unconstrained Intelligent Drug Engineering (GUIDE) project has an opening for a Senior bioengineer who is responsible for the efficient, smooth, and effective management of a team and simulation- and machine-learning-driven computational pipeline to redesign antibodies.
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Knowledge of best practices in software engineering for reproducibility and data management skillsAbility to work in and adapt to a fast-paced environment Preferred Qualifications: Experience with protein co-evolution analysisExperience with NGS and bioinformatics toolsExperience with agile methodologies.
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The ML Research Scientist will also have a hands-on roll and is expected to customize and create various machine learning algorithms to operate over multi-domain data and optimizing the performance of those algorithms on the data.
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What you’ll bring to Circle: 5+ years of experience in data analysis or data science, with 3+ years focusing on machine learning problems, ideally in a relevant space (KYC, sanctions detection, anti-fraud detection, treasury management, crypto/blockchain data science.
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This role requires experience in Azure OpenAI, Large Language Models (LLMs), Python, and proficiency in developing applications using Streamlit, LangChain, Databricks, and Python frameworks such as Flask and FastAPI. The ideal candidate will be adept at building scalable machine learning models and deploying them to production environments and also being part of customer success teams.
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