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Design and implement deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data. As a Signal Processing Engineer, focused on Deep Learning, you will be part of a cross-functional team composed of Signal Processing, WHOOP Labs, Firmware, and Data Science.
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Support AAG leadership in extending and growing our machine learning, engineering and analytics capabilities. Advanced Degree in a quantitative discipline such as Computer Science, Engineering, Physics, Statistics, Applied Mathematics, etc.
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Drive cross-functional collaboration across our data science, machine learning, lab informatics, and medicinal chemistry teams to advance our computational infrastructure for our drug discovery and development mission.
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Bachelor’s degree (or foreign education equivalent) in Applied Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Information Management, or a closely related field and three (3) years of experience as a Senior IT Data Analyst (or full stack engineering or closely related occupation) performing data analysis and modeling using SQL, PL/SQL, and Python in a financial and brokerage services domain.
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Bachelor's or Master's STEM degree - computer science, data science, economics, mathematics, actuarial sciences, engineering, or physics preferred. Senior Data Analysts support complex consulting projects and partake in the extensive validation and finalization process of KCC's catastrophe computer models, as well as developing internal tools and automations.
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There are various disciplines involved in Enterprise Data & Analytics, including: data source identification and analysis, data engineering, data visualization & data science/machine learning.
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PhD or MSc in computational biophysics, computational chemistry, chemical engineering, computer science, machine learning or related field. Development and implementation of advanced data analytics and state-of-the-art machine learning models to generate insights from proteomics (ABPP) data, protein structure, and protein dynamics to enhance target prioritization and structure-based drug design efforts.
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Expected Qualifications: Demonstrated knowledge and teaching experience in at least one of the following areas: Statistics, Data Analytics, Data Visualization with different tools, Python, Data Management & SQL, Data Engineering, R Programming, and/or Machine Learning.
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You will partner closely with the leaders of Onyx’s engineering teams (DevOps and Infrastructure, AI/ML analysis and computing platform, data & knowledge platform, data engineering, UI/UX engineering), along with the Onyx portfolio management team, to deliver industry-leading Data Engineering products that power downstream consumption of petabytes of business-critical data.
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Masters or Bachelors degree in quantitative field such as Data Science, Economics, Engineering, Finance, Statistics, Mathematics or Physics. Knowledge of statistical sampling, optimization, machine learning, predictive modeling, and artificial intelligence techniques.
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Strong understanding of data engineering tools (Spark, Airflow, PySpark, Kafka, Hive, SQL/NoSQL); working knowledge with at least one programming language (Python, Java, Scala) Cross-Functional Collaboration: Collaborate with R&D customers, R&D Digital & Tech organization, Onyx DevOps & Infrastructure, data engineering, computing platform engineering, data & knowledge platform engineering, program management teams to align product strategies, gather input, and drive successful implementation plans.
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Bachelor's (and at least 3 years of experience working in AI/ Data Science) or Master’s degree in Computer Science, Engineering, Mathematics, or related field. Continuously monitor and improve the performance of machine learning models through data analysis and testing.
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Stay abreast of latest AI trends and maintain state-of-the-art knowledge in machine learning, deep learning, and computational linguistics. PhD in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field.
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The Algorithms, Machine Learning, & Data Science Group within AES Advanced Tech Team creates advanced algorithms in the fields of machine learning, artificial intelligence, signal processing, intelligent control, and other areas of electrical engineering and computer science that are of strategic interest to the AES Business Unit’s goals and roadmaps.
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Ph. D. in Bioengineering, Biochemistry, Biostatistics, Chemical Engineering, Computer Science, or similar discipline, with 0-5 years of experience and a strong focus on deep learning and/or cell engineering.
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learning data engineering computer jobs Title: analyst in Boston, MA
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