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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 leveraging complex data to drive business decisions, hands-on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Python, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Experience with statistical and mathematical modeling, artificial intelligence and machine learning software and methods. Extensive experience or knowledge of cloud-based data platform technologies such as AWS, Microsoft Azure or Google Cloud Computing.
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This Expert brings significant experience in crafting, developing, training, and delivering machine translation data sets, training models, and machine learning algorithms.
$265,200 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Data Science Management, Director Data Science, Director of Data Science, Director of Engineering, Senior Director of Data Science, Senior Director Engineering, Manager Data Science, Manager Engineering, Artificial Intelligence Management.
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Build AI Solutions: Lead a team of data scientists working with machine learning engineers to build AI solutions leveraging traditional machine learning models as well as modern Generative AI and LLMs. These solutions will be both internal employee-facing and external customer-facing.
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Key Responsibilities: Lead the engineering team in designing, developing, and deploying solutions applying modern web frameworks, machine learning solutions, data engineering technologies, microservices, and cloud-native architecture.
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2+ years of experience with OpenSearch, Kafka, Vue.js and some exposure to Machine Learning. At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud.
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You will be at the center of a fast-growing, high-tech startup using the latest Cloud technology, AI (Artificial Intelligence), and machine learning. Our modern tech stack includes Kafka, Elasticsearch, AWS, MongoDB, Kubernetes, ArgoCD, Java 11, Spring, Python, React, and others.
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Expertise in scientific programming languages (e.g., Python, R) and libraries (e.g., PyTorch, Tensorflow), cloud-based computing, and data manipulation. Senior Principal Scientist, Machine Learning, Molecule Design and Prediction Status: Archived.
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As a data scientist for PRS, you will develop predictive modeling / machine learning solutions to complex business problems and create value to the business. If the candidate does not have a background in Actuarial science, the candidate should have 4+ years of industry experience building and analyzing machine learning models and should hold a graduate degree in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Economics or Engineering.
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Working in a team setting, this position applies deep knowledge of advanced artificial intelligence and machine learning algorithms and models to solve complex problems involving biological, genomic, clinical and healthcare data within a setting of advanced cancer screening and precision oncology.
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Desire proficiency in cloud-based computing and data platforms (Azure, Google Cloud, BigQuery, Snowflake, DataBricks) Advanced level skill with SQL, Excel, data visualization (Tableau preferred), query tuning, various database models (normalized / dimensional / data vault / etc.
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Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. 5+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL.
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You will also be building advanced analytics solutions while leveraging various cloud technologies in partnership with the Tech Product Team and Data Scientists to robustly scale up AI/Machine Learning models.
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