Data Engineer / Data Scientist...

IBMKenexaRPO IBM Kenexa

IBM is seeking Data Engineers/Data Scientists to combine cognitive computing/Watson technology with traditional data science/engineering and apply it at scale to transform enterprise business processes including: Operations, Marketing & Communications, Finance & Accounting, Supply chain, Human Resources. As such, the ideal candidate will not just address business problems through the application of data technology but also be able to modify a traditional open-source or other data stack to incorporate cognition. In short, the candidate can create and apply technology to enable the transformation of IBM's business processes.

The ideal candidate will have significant experience with much of the following or equivalent:

Modeling and mining large data sets using open source technologies such as Programming language (R), Hadoop, Apache Spark, etc.

Software development experience with Jaql, Hive, Java, Go, C++ , JSON, Python, XML etc.

Cloud-based data engineering experience with PaaS & IaaS

CUDAs, FPGAs & HPCs, applied to data science and deep learning

Deep learning methods and techniques

Creating and deploying large-scale, data-driven systems

Different data mining techniques – associations, correlations, evidence, inferences, clustering, support vector, ensemble methods, GBM, etc.

Cloud-based, agile, devops environments

Creation/deployment of models and algorithm to analyze social, machine, text, sensor, streaming, large-volume unstructured data.

Data science, data engineering, statistics, modeling, operations research, computer engineering, computer science and applications, or mathematics.

Innovating experimental design and measurement methodologies.

Innovating modeling, machine learning, entity linkage, knowledge graph and similar approaches.

Automatically find and interpret data rich sources, merge data together, ensure data consistency, and provide insights as a service.

Optimized management of big data within set hardware, software and bandwidth constraints.

Designing and deploying user interfaces that interact naturally with people.

Required Technical and Professional Expertise

  • At least 5 years of experience in one or more of the following:

Modeling and mining large data sets using open source technologies such as Programming language (R), Hadoop, Apache Spark, etc.

Software development experience with Jaql, Hive, Java, Go, C++ , JSON, Python, XML etc.

Cloud-based data engineering experience with PaaS & IaaS CUDAs, FPGAs & HPCs, applied to data science and deep learning.

Different data mining techniques – associations, correlations, evidence, inferences, clustering, support vector, ensemble methods, GBM, etc.

Creation/deployment of models and algorithm to analyze social, machine, text, sensor, streaming, large-volume unstructured data.

Data science, data engineering, statistics, modeling, operations research, computer engineering, computer science and applications, or mathematics.

Deep learning methods and techniques.

Creating and deploying large-scale, data-driven systems.

  • At least 5 years experience in one or more of the following:

Cloud-based, agile, devops environments.

Innovating modeling, machine learning, entity linkage, knowledge graph and similar approaches.

Automatically find and interpret data rich sources, merge data together, ensure data consistency, and provide insights as a service.

Optimized management of big data within set hardware, software and bandwidth constraints.

Innovating experimental design and measurement methodologies.

Designing and deploying user interfaces that interact naturally with people.

Eligibility Requirements

  • Educational Requirements:

PhD – data science, data engineering, statistics, modeling, operations research, computer engineering, computer science, mathematics, or related discipline

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