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Conduct ongoing research to stay abreast of the latest developments in machine learning, deep learning, and data science, and apply this knowledge to enhance project outcomes.
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They will use relevant statistical techniques, machine learning models and artificial intelligence algorithms to analyze data, design data models and derive insights to influence actions that maximize business value and effectiveness for the commercial technology segments.
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Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment. AWS, AlienVault, Azure, Bash, Confluence, EKS, GSuit, Github, Golang, HTML, Hashcat, JIRA, JWT, Java, Javascript, Jenkins, Kubernetes, Metasploit, New Relic, Nmap, NodeJS, OWASP, Okta, PCI-DSS, PagerDuty, Python, Rails, Ruby, SAML, SQL, SQLMap, SignalScience, SignalSciences, Slack, Snyk, Sumologic, tenable.io, Twistlock, TypeScript, Wireshark, tcpdump.
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Understanding of or experience with implementing Search and/or Data Science/Artificial Intelligence related software solutions. + Understanding of microservices architecture, preferably using Open Source technologies like Apache Solr, Apache Spark, Kubernetes, Python, and TensorFlow.
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The Clinical Data Science team at Eisai, Inc., is expanding its capabilities by acquiring in-house talent related to bioinformatics, machine learning, and big data analytics for the Oncology and Neurology clinical programs.
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Preferred: Experience designing tests for distributed systems and large-scale data pipelines. You will primarily develop on / interact-with Google Cloud Platform and other technologies such as BigQuery, Airflow, Python, SQL, Spark/PySpark, Linux, Docker, Kubernetes.
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Bachelor's degree in a quantitative discipline: statistics, applied mathematics, computer science, data mining, machine learning, or some other empirical science; in lieu of a Bachelor's degree, candidates with 9+ years of equivalent experience will be considered.
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The company is using the power of next-generation sequencing, population-scale clinical studies, and state-of-the-art computer science and data science to enhance the scientific understanding of cancer biology, and to develop its multi-cancer early detection blood test.
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Demonstrated expertise in statistical theory and a wide range of statistical methods and machine learning techniques for high-dimensional multi-omics data. Develop and publish relevant bioinformatics methodologies, pipelines, and algorithms, and stay abreast of the latest advancements in bioinformatics, machine learning, and oncology research.
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Experience with supporting data science / machine learning data pipelines. Familiarity with Golang, Python, and R.Familiarity with Clinical Data Model standards (e.g. OMOP, CDISC) and Healthcare Data standards (e.g. FHIR, HL7 v2.
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Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data- oriented programming languages (Python, R) and visualization software such as Qlik.
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Data Science Integration: Utilize data science methodologies and tools to enhance analytical capabilities, including predictive analytics and machine learning models, to forecast trends and inform strategic directions.
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Proficiency in statistical methods (e.g., linear regression, logistic regression, survival analysis, ARIMA), AI/machine learning techniques (e.g., Random Forest, XGboost, Clustering, Neural Network, NLP, Image Recognition) and programming languages (e.g., Python, R, SAS, SQL.
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In-depth programming knowledge in all or some of the following: Programming Languages: Java (J2SE & J2EE), Python, Servlets, JSP, JSTL, EJB, JDBC, JMS, JNDI, JAXB, JAX-WS, JAX-RPC, Spring/Struts/Hibernate, Elastic Search, Web Technologies: MEAN Stack, Angular JS, NodeJS, AJAX, JQuery, XSLT, JavaScript, HTML, DHTML and CSS, Database/Data warehouse technologies: Snowflake, Oracle, SQL Server, Mongo DB, Elastic Search.
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Familiarity with high throughput screening (HTS) data, toxicology studies, machine learning and experimental design as needed for genomics studies, dose-response, and environmental health studies.
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