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A Master' degree or PhD in Computer Science, Statistics, Applied Mathematics, or a related field, with at least 5 - 7 years' experience in data science or a similar role.
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The Data Science team at Meijer leads the strategy, development and integration of Machine Learning and Artificial Intelligence at Meijer. Build production ready prototypes for, and iteratively develop, end-to-end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet end user needs.
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Economics, Statistics, Applied Mathematics, Mathematics, Financial Mathematics, Data Processing/Analytics/Science. As a Senior Associate in our Risk Analytics and Modeling team, you’ll have the opportunity to apply your quantitative, modeling and analytical skills in a real world environment to develop or validate statistical, financial engineering, and AI/machine learning models in the areas of credit risk, market risk, assets and liabilities management, fraud detection, anti money laundering and other functional modeling and analytics area.
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Our team develops, implements, and maintains recommendation and personalization algorithms for Disney Streaming's suite of streaming video apps, notably Disney+ and Hulu. As a member of this team you will collaborate across Engineering, Product, and Data teams to apply machine learning methods to meet strategic product personalization goals, explore innovative, cutting edge techniques that can be applied to recommendations, and constantly seek ways to optimize operational processes.
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7+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience. In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings.
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Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop and implement in production algorithms for personalization, recommendation, and other predictive systems; maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams.
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MS or PhD in statistics, math, computer science, or related quantitative field. Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate.
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Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience. Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark.
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Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets.
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Production experience with developing content recommendation algorithms at scale and familiar with metadata management, data lineage, and principles of data governance. Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
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Building streaming data pipelines using Kafka, Spark, or Flink. Collaborate with product and business stakeholders: Identify and define new personalization opportunities and work with other data teams to improve how we do data collection, experimentation and analysis.
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Proven publications in machine learning, AI, computer science, statistics, applied mathematics, data science, or related technical fields. Individuals in this role are expected to be recognized experts in areas such as artificial intelligence, machine learning, computational statistics, and applied mathematics, particularly including areas such as deep learning, graphical models, reinforcement learning, computer perception, natural language processing and data representation.
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Masters degree in Applied mathematics, Computer Science, Engineering or related field and 6 years of industry/academic experience. Doctorate degree in Applied mathematics, Computer Science, Engineering or related field and 3 years of industry/academic experience.
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Data Science, machine learning or predictive analytics experience. Hands-on experience working with modern data technologies stack, such as Databricks, DBT, Google BigQuery, Redshift, RDS, Snowflake or similar solutions.
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Expert level in Designing and Architect solutions in Azure Data factory, Azure Databricks, Azure Datalake, Delta Lake and Azure synapse analytics implementation. Have good experience configuring Microservices using Docker, Kubernetes on Azure Data Bricks.
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machine learning applied mathematics computer science data jobs Company: Cleveland Clinic in Anoka, Minnesota
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