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Five or more years of industry experience in data science with a focus on data analytics, machine learning, and deep learning. Model Integration & Scalability: Collaborate with cross-functional teams to integrate data science solutions into scalable, production-grade systems using open-source frameworks like Apache Spark and containerization technologies like Docker.
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Proven expertise in Apache Spark, Apache Beam, and Airflow, with a deep understanding of distributed computing and data processing frameworks. Architect and develop large-scale, distributed data processing pipelines using technologies like Apache Spark, Apache Beam, and Apache Airflow for orchestration.
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Deep familiarity with cloud computing platforms like AWS, Azure, and GCP, and their respective data science ecosystems (e.g., AWS SageMaker, Azure Machine Learning, GCP AI Platform.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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We are hiring talented Senior Data Engineers in roles related to building cloud-based data pipelines for machine learning, data processing with Apache Spark, and database development.
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Tops 3 Skills Needed Data Engineering 5 years Data Bricks 5 years Py Spark 5 years Years of Experience: 12 years Technology requirements: Proficiency in Apache Spark, including Spark Core, Spark SQL and Spark Streaming.
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Kafka, Apache Pulsar, RabbitMQ, Amazon Kinesis, Apache Flume, Apache Storm, Apache Spark Streaming, Google Cloud Pub/Sub. Growth mindset is encouraged and the team offers leadership opportunities at any level.
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Direct Client - Sr Lead Data Engineer Apache Spark, Spark Core, Spark SQL, Spark Streaming – Data Bricks Location: Hybrid (Seattle, WA) Length: 12 Months Job description This position contributes to Client success by building enterprise data services for analytic solutions.
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Demonstrable experience in machine learning, deep learning, NLP, computer vision, reinforcement learning, and/or other AI domains. Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced machine learning models, with a special emphasis on Generative AI. In this role, you will craft and refine AI-driven solutions, turning innovative ideas into value-adding features and services, thereby solidifying our market leadership and technological forefront for our clients.
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In this role, the Machine Learning Engineer will work with a variety of data-driven technologies, including traditional and deep learning paradigms. The Machine Learning Engineer is responsible for proposing, planning, executing, and analyzing research and development machine learning projects related to the field of advanced manufacturing artificial intelligence.
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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. This position is with the Signal Processing team at WHOOP. 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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Develop state-of-the-art Audio AI solutions using combination of classical signal processing and machine/deep learning-based approaches. + Broad knowledge of machine- and deep-learning algorithms and principles and state-of-the-art methods.
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Preferred: Proficiency in common machine learning programming languages such as Python, R, and Spark, and familiarity with various machine learning algorithms. Experience in building and deploying statistical machine-learning models, such as linear regression, logistic regression, GLM, GAM, etc.
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Skill :- Data Modeling, ETL Solution, Data Mart, Data Lakes, DWH, Data Analytics Knowledege of programming language (e.g., SQL, Python & Apache Spark, SAS & R), Database (e.g., Oracle, Postgres, Hive, and HBase), ETL/Orchestration tools (e.g., Informatica, Autosys, and Airflow, etc.
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Skills Required: Requires experience in the following: Linux; Agile SDLC; Data Architecture Disciplines; Microservices; Apache Kafka; Docker; J2EE; Jenkins; Spring; Hibernate; Java; Python; Shell Scripting; SQL; XML; Apache Tomcat; Bootstrap; REST; Maven; JSON; Kubernetes; Apache Zookeeper; AWS Cloud Services; Dynatrace; Cassandra; Hadoop; Hive; Oracle; Teradata; Apache Spark; Splunk; GIT; Junit; Unit Testing; Snowflake; Terraform.
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