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Copernicus Data Processing Service Engineer
2021-07-08 00:13:09| Space-careers.com Jobs RSS
For our customer, EUMETSAT in Darmstadt, Germany, we are seeking to build up a team of data processing engineers to support their data processing activities of the ground segment for existing and future missions. For this we aim for a mixed team of entrylevel as well as more experienced data processing engineers who are willing and eager to take on this exciting opportunity in the Space industry. Responsibilities Your tasks as part of the data processing engineering team will typically include but are not limited to Participate to the activities in the area of data processing in support of operations Provide inputs and contribute to relevant technical and progress meetings, to relevant S3 PDGSPDP, S6 PDP and system level reviews and milestones. Participate to the interaction with relevant partner organisations Provide expertise to the evolution of test reporting tools necessary for the ongoing activities, such as the ATE Automatic Test Environment. Contribute to software and hardware configuration deployment, configuration control and verification activities Contribute to the implementation, maintenance and evolution of the SW for S3 PDGSPDP and S6 PDP reporting. Provide expertise and contribute to data processing system level engineering activities Acquire indepth knowledge on assigned S3 PDGSPDP and S6 PDP components maintaining an up to date knowledge at processing and ground segment level. Provide training to the members of the SEP Data Processing Competence Area on S3 or S6 elements where needed and when requested. Contribute to Anomaly processing activities where possible Contribute to documentation activities, such as e.g. the technical baseline documentation ADD, ICD, STB, etc.. Contribute to other Software engineering tasks as required. This is a fulltime position to be partially located at EUMETSATs premises in Darmstadt, Germany and partially at the Terma GmbH office in Darmstadt. Expected start date October 2021 or ASAP thereafter. Oncall support or additional services outside normal office hours may be requested by the customer and will be agreed between the customer and the service provider. Qualifications Competencies Besides a university degree or equivalent in a relevant discipline, each member of the team shall meet the following requirements Essential skills to have Proven experience with data processing Level0, Level1 and Level2 of meteorological andor earth observation products from Satellites instruments Proven experience in the integration, verification and validation IVV of data processing systems andor processors, including generation of test plan, test specification, procedures and test reports for endtoend processing chains Knowledge of scripting languages and databases e.g. Perl, Python, shell, PostgreSQL Demonstrated experience with UNIXLinux operating systems Ability to write accurate and consistent technical documentation Practical knowledge in Configuration Control High degree of working autonomy Problem solving Good analytical skills High degree of interpersonal awareness University degree or equivalent in a relevant discipline Skills and experiences considered an asset Knowledge of Spacecraft andor ground segment operations Knowledge of programming languages e.g. CC, Java and IDL Knowledge of Knowledge andor experience of quality control, calibration and validation Knowledge of Development and maintenance of operational procedures andor configuration data Knowledge of ECSS standards The team also needs to be able to cover the following requirements Proven experience in System Engineer development and system design Knowledge in Computer systems, Network systems and IT Security Experience in working as part of a multicultural team Ability to manage a service team of similar size, preferably in a similar environment to EUMETSAT for SM What can Terma offer At Terma, we consider skilled employees, enthusiasm and job satisfaction as the very foundation of our success and as a prerequisite for the development of the bestinclass solutions that Terma provides. We lead the way in applying new technology, offering a wide range of growth opportunities for each individual and emphasizing mutual respect across the board in our workplace. Terma offers you a pleasant working environment at the customer site, where you will be able to take on challenging tasks and responsibilities in a highly professional company. Great opportunities for training and personal development Challenges in advanced technical environment International and cosmopolitan working atmosphere An employment contract with an attractive package with extralegal benefits Highly competitive salary Additional information For further information, please contact Mrs. Raluca Moise by telephone 49 6151 860050 or by email recruitment.determa.com. Closing date August 27, 2021 Please note that applicants must hold all appropriate documentation and permits to work in Europe. Recruitment is depending on successful selection by the customer.
Tags: service
data
processing
engineer
Instrument Data Processing Engineer
2021-07-07 13:13:12| Space-careers.com Jobs RSS
Deadline to apply 21st July 2021 With 2.100 employees worldwide, the CS Group is a leading IT company in France and in Germany, and a prime contractor in designing, integrating and operating mission critical systems. It has now been over 30 years that comprehensive CS Group solutions for space systems and applications, both on the ground and in space, have been part and parcel of this odyssey. Our German activities are growing at a fast pace and to face our customers high level expectations and needs, we are looking for talented profiles with relevant experience in Space systems and applications. The key person will support our customer during routine operations of Copernicus Sentinel Spacecraft including the operations buildup and the operations, engineering and scientific activities to be undertaken as part of the routine operations of the satellite system for the Copernicus Sentinel Mission. PROFILE Mandatory skills In addition to having an University degree or equivalent in a relevant discipline, the Key Person shall have the following mandatory skills At least 3 years experience ofthe operation of realtime, complex, satellite instrument data processing systems Demonstrated experience of Instrument data and product processing Level0 to 2 of meteorological andor earth observation products Demonstrated experience in near real time, data driven, automated systems with large computer networks Significant experience of validating complex remote sensing data processing systems Demonstrated experience with UNIXLinux operating systems. Additional skills considering as an added value Imaging sounding surface topography instrumentation for remote sensing spacecraft Development and maintenance of operational procedures configuration data Programming languages e.g. C, C and IDL Scripting languages and databases e.g. Perl, Python, shell, Oracle Spacecraft andor ground segment operations Knowledge andor experience of geophysical remote sensing data processing, calibration and validation. TASKS The key persons Instrument Data Processing Engineers shall perform tasks related to instrument data processing in support of the Copernicus Payload Data Ground Segment PDGS, during current routine operations, as well as for the operations build up and the routine operations phase for the dual satellite system. The tasks cover the operation of the different data processing components, which include Data ingestion, processing and product distribution Data and Product analysis Operational performance monitoring and reporting, including the development, documentation and maintenance of the relevant tools for e.g. production statistics generation and reporting, PDGS monitoring and reporting The testing and the implementation of new products The settingup, implementation and update of operational procedures and documentation Anomaly investigation First line maintenance, including oncall support Coordination with external organisations and service providers Attendance at technical meetings Participation to formal reviews.
Tags: data
processing
instrument
engineer
Earth Observation Data Products Operation and Monitoring Engineer
2021-07-07 09:12:25| Space-careers.com Jobs RSS
At Serco, we strive to promote and enable the diversity, development, wellbeing and safety of our people. We understand that healthier, happier employees go handinhand with strong business performance, enhanced productivity and better outcomes for those we serve. We want everybody who works for Serco to have a positive experience and access to opportunities to develop in their chosen careers. Dedicated to the European Space Agencys Earthobserving activities, ESAESRIN is the European centre of excellence for exploitation of Earth observation missions. The mission and payload operations of ESAs Earth observation satellites are managed here, and ESRIN is the primary source for the acquisition, distribution, and exploitation of data from these and other nonESA satellites. Serco Italy has a long history of providing Earth Observation operations, maintenance and frontend services to ESA. To provide continual service improvement, Serco has recently introduced a special focus on evolution and innovation, with the aim of supporting ESA in further evolving the services and products. Does working in a Serco environment sound appealing to you? We would love to hear from you and your interest in the new opportunity of Earth Observation Data Products Operation and Monitoring Engineer in Frascati ! Does this vacancy not suit your profile? Not to worry, you can send your updated CV to careers.italyserco.com a member of our Recruitment Team will come back to you shortly after. Job Description Joining Serco Italys amazing team offers Competitive Salary Great career opportunities in the Space industry Competitive Salary Corporate Benefits Package Exciting relocation package if applicable Company events International environment Main Responsibilities Are you looking to secure a career in a public sector environment? We would love to hear from you! As Earth Observation Data Products Operation and Monitoring Engineer you will be responsible for The EOData products Operation and Monitoring Engineer will support the implementation and the routine operations of Earth Observation processing chains and the related information extraction for Database population. The role encumbers in the following areas Earth Observation EO data processing management Systematic extraction of information from EO data products. Data Information System population Reporting to the customer. Technical investigation and root cause analysis activities In return, we offer a friendly, supportive and professional environment that respects your worklife balance and ultimately contributes to the delivery of public services in Belgium and around the world. Successful Candidate Mandatory skills EO data products and auxiliary knowledg Good Linux O.S and native shell scripting Java or Python programming Understanding of XMLXSD Problemsolving and creativityoriented attitude Technical documentation preparation Good communications skills in English spoken, written and possibly good knowledge of Italian Ability to work autonomously and under strict schedule Any experience in the following would be considered an asset SQL knowledge Tableau Additional skills Proactive approach to problem identification and resolution Very good user liaison capability and teamplayer spirit At least 2 years of professional experience Important Any offer of employment is contingent upon you providing documents to verify your identity and employment eligibility, as required by law. Applicants are reminded that they will be requested to produce such documentation during the recruitment process. Please contact a member of the recruitment team if you require further details of acceptable types of documentation required for verification of identity and work authorization. Data Protection When creating a profile on the Serco Career Centre you agreed to the Data Protection policy, a copy is available upon request.You may submit a written request revoking your consent to this agreement at any time. About The Company Why should you join Serco ? At Serco not only is the nature of the work we do important, everyone has an important role to play. Meaningful and vital work Youll contribute to methodologically intercepting challenges whilst achievements will also be recognised and celebrated. A world of opportunity Youll be wholeheartedly supported with development and career progression Great people Youll become an integral member of a welldefined and supportive team who believe passionately in the value of our work. What we offer Chance to contribute to innovation in the public services sector A company passionate about diversity and inclusion Permanent employment with comprehensive Serco Benefits package. Pension About Serco At Serco, not only is the nature of the work we do important, everyone has an important role to play when managing complex public services. We are a team of 50,000 people responsible for delivering essential public services around the world, we are innovators, committed to redesigning and improving public services for the benefit of everyone. By joining Serco you will have unlimited access to our Global Employee Networks SercoInspire Gender, SercoEmbrace Multicultural, SercoUnlimited Disability and InSerco LGBT Networks. Serco Employee Networks, led by colleagues who are passionate about diversity, inclusion and belonging. Apply Please click on the apply button to be taken to our careers website Serco is a Disability Confident Employer committed to employing and retaining people with disabilities. Disabled applicants who meet the minimum criteria for the job will be given the opportunity to demonstrate their abilities at an interview.
Tags: data
products
operation
earth
MTS acquires large data centre in Moscow region for RUB 5.2 billion
2021-07-06 09:39:00| Telecompaper Headlines
(Telecompaper) Russian operator MTS has bought GDTs Energy Group for RUB 5.2 billion, including net debt...
Tags: data
large
region
centre
Speeding up the machine learning lifecycle to get more from your data
2021-07-02 21:20:27| The Webmail Blog
Speeding up the machine learning lifecycle to get more from your data nellmarie.colman Fri, 07/02/2021 - 14:20 Businesses are realizing the value of using machine learning models to drive better outcomes. Harnessing the predictive power of your data with machine learning models is becoming more critical to business operations, yet 60% of machine learning models never make it to production. Where is it all going wrong? Widespread struggles with AI and machine learning We conducted a global study in December 2020 and January 2021 on AI and machine learning adoption, usage, benefits, impact and future plans. The study surveyed 1,870 IT leaders in various industries across the Americas, Europe, Asia and the Middle East. The study revealed that the majority of respondents (82%) are still exploring how to implement AI or struggling to operationalize AI and machine learning models. The research also showed that, on average, companies have four AI and machine learning R&D projects in place and we know from speaking to customers that most organizations are investing in research and development into model development. However, the disconnect between the operations or data ops teams and the machine learning engineers or data science teams means that many of the models never make it to production. There are often issues around deployment, automation and scalability of machine learning models. The challenges of operationalizing machine learning models Data science teams often face challenges in how they manage models as they pass through different stages of the machine learning workflow. Getting machine learning models swiftly from a development environment to production is not an area of expertise for data scientists. A DevOps or infrastructure team would be better equipped to deliver on the reproducibility of models and predictions. It can be difficult to reproduce a models output when moving it from one environment to another as it requires careful tracking of library versions, data sets, diagnostics, performance monitoring and model drift. Another common problem is that models tend to multiply into different environments and become difficult to keep a track of. Data scientists create domain-specific models and run many experiments, first starting in a development environment, and then moving them along the chain into a testing environment. This results in multiple models running simultaneously across different environments, using different data sets and different hyperparameters. So this makes it almost impossible to track a model's lineage. One of the most important aspects of governance and regulatory compliance (especially if you're dealing with any kind of auditors) is tracking and explaining everything your model is doing or has done. DevOps is not enough The DevOps culture and application lifecycle management have become a standard in the IT industry over the last decade. It emerged to fill the gap between an organization's ability to develop application code and the way to efficiently deploy, test, scale, monitor and update workloads. Mature CI/CD pipeline needs are largely addressed in application development by standardized tools and best practices that are already in place. Unlike application development, where quality comes from the code itself, the quality of a machine learning model comes largely from the data features used to train it. The importance of these data features cannot be understated as their quality drives your machine learning models performance. And it's worth mentioning that machine learning models are still in their operational infancy. Additionally, data might change daily, and data that was used for predictions that you did for today might be significantly different from the data used for model training a month ago. In this case, the production model needs to be retrained and go back into the development phase. So as a result, a machine learning models lifecycle is significantly different from an application lifecycle. We had a customer in the fraud space who wanted to push production models every 24 hours to account for new threats. The customer would retrain and redeploy their model every day to be able to account for any drift in data. That's impossible to do without a mature solution in place. Introducing the Model Factory Framework The machine learning lifecycle is complex. There are many steps to an entire machine learning lifecycle such as data ingestion, data analysis, data transformation, data validation, data splitting, model building, model training and model validation. And with all these steps there are associated challenges. This is why we developed the Rackspace Technology Model Factory Framework. The Model Factory Framework is built on AWS, using open source tools that enable rapid development, training, scoring and deployment models. The Model Factory Framework was built to address any problems you face when taking machine learning models from development to production. The Model Factory Framework simplifies the whole machine learning lifecycle which usually has over 25 steps and can take months to 10 or so steps which can be completed within a matter of weeks. Learn more about the Model Factory Framework If you would like to learn about the Rackspace Technology Model Factory Framework in more detail and explore how it improves processes from model development to deployment, monitoring and governance view our webinar, Automating Production Level ML Operations on AWS. In this webinar we'll cover: Introduction to MLOps Foundations powered by Model Factory The gap between the Data Scientists and ML Operations The distinction between MLOps and DevOps Architecture patterns necessary for elements of effective MLOps How a model factory architecture holistically addresses CI/CD for ML Speeding up the machine learning lifecycle to get more from your dataDiscover how a model factory framework can simplify your entire machine learning lifecycle, cutting the time required from months to weeks. Automating Production Level ML Operations on AWShttps://www.brighttalk.com/webcast/17680/463764Watch the webinar
Tags: your
data
learning
machine
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