Data Scientist jobs in United States

The role

We are looking for a talented Machine Learning Scientist or Atmospheric Composition Scientist with AI/ML experience to contribute to the use of AI/ML in atmospheric composition monitoring and forecasting. This includes the exploratory development AIFS-COMPO, the use of AI/ML based emulators of the relevant chemical and physical processes in IFS-COMPO, and the potential application of AI/ML for the global monitoring of emissions and surface fluxes. As a Scientist for Machine Learning (A2) you will be embedded into both CAMS and AIFS teams and be supported by domain experts from both disciplines. You will explore how the AIFS or elements of the traditional IFS-COMPO should be adapted and trained to leverage atmospheric composition datasets, such as analysis and reanalysis datasets, as well as how these new techniques could be used for the monitoring of emissions and natural fluxes. You will be at the forefront of understanding the role of machine learning for atmospheric composition monitoring and forecasting.

The challenge

ECMWF is building a world-leading, machine learning based probabilistic weather forecasting system (AIFS), to complement our existing physics-based system (IFS). We are pioneering the operationalization of machine learning forecasting models in this domain. ECMWF now runs both deterministic and probabilistic AIFS forecasts daily, providing open data and products to users around the world.

Global atmospheric composition forecasting with the IFS is also a key service of ECMWF. The forecasts are a core component of the Copernicus Atmosphere Monitoring Service (CAMS). The IFS is today one of the most comprehensive and advanced systems in the world for data assimilation and forecasting of global atmospheric composition, with operational outputs serving hundreds of users daily and being seen by millions (for instance through CNN, Windy, and other platforms). Early results in the scientific literature suggest that data-driven forecasting could be a valuable contribution to the operational forecasting of atmospheric composition.

The operational CAMS data assimilation and forecasting system (IFS-COMPO) is used for the monitoring and forecasting of air quality, monitoring the ozone layer, and increasingly the monitoring of anthropogenic emissions of greenhouse gases and atmospheric pollutants. ECMWF is therefore expanding the scope of the AIFS and explore how data-driven forecasting can complement physical and chemical models in producing impactful operational forecasts of atmospheric composition. In addition, the emulation of chemical and aerosol schemes with AI/ML approaches in a hybrid IFS-COMPO configuration is being explored as well.

Your Responsibilities

• Define and build atmospheric composition training datasets.
• Explore ML emulation within IFS-COMPO.
• Contribute to AIFS/Anemoi codebase developments to expand functionality to support atmospheric composition and emissions modelling.
• Train AIFS atmospheric composition models.
• Contribute to the evaluation of AI/ML-based atmospheric composition models.
• Provide technical and scientific input to user support and training activities that are within your area of expertise.
• Provide technical management of related external CAMS contracts
  
  

What We Are Looking For

• Excellent analytical and problem-solving skills with a proactive and constructive approach.
• Flexibility, with the ability to adapt to changing priorities.
• Ability to work autonomously and as part of multidisciplinary and geographically distributed teams.
• Excellent interpersonal and communication skills.
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines.
• Genuine interest in ECMWF and Copernicus, especially on atmospheric composition monitoring and forecasting.
  
  

Your profile

• Advanced university degree (EQ7 level or above) or equivalent professional experience in computer science or engineering, computational science, physics or natural sciences, mathematics, or a related discipline.
• Experience with atmospheric composition modelling would be desirable.
• Experience developing and training large-scale neural networks in PyTorch (or similar framework) would be desirable.
• Experience in spatial-temporal modelling with neural networks would be desirable.
• Experience developing with Python is required.
• Experience with geospatial data handling with Python would be an advantage.
  
  

Candidates must be able to work effectively in English. A good knowledge of one of the Centre’s other working languages (French or German) is an advantage.

We encourage you to apply even if you feel you don't precisely meet all these criteria.

About ECMWF

The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world leader in Numerical Weather Predictions providing high-quality data for weather forecasts and environmental monitoring. As an intergovernmental organisation we collaborate internationally to serve our members and the wider community with global weather predictions, data and training activities that are critical to contribute to safe and thriving societies.

The success of our activities depends on the funding and partnerships of our 35 Member and Co-operating States who provide the support and direction of our work. Our talented staff together with the international scientific community, and our powerful supercomputing capabilities, are the core of a 24/7 research and operational centre with a focus on medium and long-range predictions. We also hold one of the largest meteorological data archives in the world.

Our mission: Deliver global numerical weather predictions focusing on the medium-range and monitoring of the Earth system to and with our Member States

Our vision: World-leading monitoring and predictions of the Earth System enabled by cutting-edge physical, computational and data science, resulting from a close collaboration between ECMWF and the members of the European Meteorological Infrastructure, will contribute to a safe and thriving society

In addition, ECMWF has established a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme, as well as being a contributor to the Copernicus Emergency Management Service. Other areas of work include High Performance Computing and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.

ECMWF is a multi-site organisation, with its headquarters in Reading, UK, a data centre in Bologna, Italy, and a large presence in Bonn, Germany as a central location for our EU-related activities. ECMWF is internationally recognised as the voice of expertise in numerical weather predictions for forecasts and climate science.

About The Copernicus Programme

See www.ecmwf.int for more info about what we do.

Copernicus is the earth observation component of the European Union (EU) space programme. Based on the exploitation of spaced based and in situ (earth-based) observations and scientific models, Copernicus provides information services for land, marine, atmospheric and climate monitoring, as well as emergency management and security. These services, and their free, open and quality assured data and tools, support a range of environmental and security applications across sectors and policy domains. For details, see www.copernicus.eu

The Copernicus Atmosphere Monitoring Service (CAMS) provides consistent and quality-controlled information related to air pollution and health, solar energy, greenhouse gases and climate forcing, everywhere in the world. The question of the environmental impacts on human health has received growing interest in recent years, in particular during the COVID-19 crisis. The UN World Health Organisation estimates that over 7 million people worldwide (over 400.000 in Europe) die prematurely due to insufficient air quality. Improving the estimates of the exposure of populations to the main pollutants is a key objective for CAMS in order to increase awareness and support the development of more protective public policies. Another key topical area of CAMS is the estimation of emissions of greenhouse gases using observations in the atmosphere: this is essential to quantify the effectiveness of mitigation policies and guide the development of new ones at city, regional and country level. For details, see https://atmosphere.copernicus.eu

Other Information

Grade remuneration: The successful candidates will be recruited according to the scales of the Co-ordinated Organisations. Full details of salary scales and allowances available on the ECMWF website at www.ecmwf.int/en/about/jobs.

Starting date: as soon as possible

Candidates are expected to relocate to the duty station. Interviews will be held by videoconference (MS Team). If you require any special accommodations in order to participate fully in our recruitment process, please contact us. Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.

Who can apply: At ECMWF, we consider an inclusive and diverse environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture. We value the benefits derived from a diverse workforce and are committed to having staff that reflect the diversity of the countries that are part of our community, in an environment that nurtures equality and inclusion.

Applications are invited from nationals from ECMWF Member States and Co-operating States, as well as from all EU Member States. ECMWF Member States and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the United Kingdom.

In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy. Applications from nationals from other countries may be considered in exceptional cases.