Team Leader - Subseasonal Team

Your role

We are seeking a Team Leader for Sub-Seasonal Forecasting to lead the ongoing scientific development of sub-seasonal forecasting at ECMWF within the Research Department.

ECMWF delivers operational forecasts to stakeholders on timescales from medium-range up to multiple seasons and beyond. Operational delivery includes world-leading numerical sub-seasonal forecasts produced using ECMWF's Earth System Model together with carefully prepared ensemble initial conditions.

About the Sub-Seasonal Forecasting Team

The sub-seasonal forecasting team is part of the Earth System Predictability Section, Research Department. The team works closely with the Seasonal Forecasting Team and currently consists of 10 scientists, majority supported by external funding. The team develops and provides scientific support to operational sub-seasonal forecast systems at ECMWF.

At present, ECMWF operates a physics-based sub-seasonal ensemble forecast system based on the Integrated Forecast System (IFS). It runs a 101‑member ensemble daily out to 45 days, with an associated set of reforecasts covering the previous 20 years. A machine‑learning based sub-seasonal forecasting system is planned for introduction later in 2026 to run alongside the physics-based forecast.

Your Responsibilities

• Lead and manage a team of scientists, driving ongoing development and improvement of ECMWF’s sub-seasonal forecasting systems, including both physics‑based and machine‑learning‑based models.
• Contribute scientific expertise to development and assessment of ECMWF forecasts at sub‑seasonal and across timescales.
• Ensure the scientific integrity of ECMWF’s sub‑seasonal forecasting systems, and that forecast system developments meet user needs.
• Represent ECMWF’s sub‑seasonal forecasting both internally and in international scientific and operational communities.
• Seek and secure external funding to support targeted research needs.

Education

• Excellent university degree and a PhD (EQF Level 8) in climate science, mathematics, physics or a related field.
• Substantial years of experience in relevant scientific research.
• Strong record of scientific publications.

Experience, Knowledge and Skills

• Scientific excellence with a strong track record in research relevant to atmospheric and Earth system sciences.
• Experience in operational forecasting research and understanding how scientific advances translate into forecast improvements.
• Domain expertise in sub‑seasonal prediction, including both physics‑based and emerging machine‑learning approaches.
• Proven team leadership skills.
• Expert knowledge of atmospheric dynamics and physical climate processes relevant to sub‑seasonal timescales.
• Extensive experience designing, running, and evaluating forecast experiments, including appropriate statistical methods.
• Proven capability to operate and work with physics‑based forecasting models in a research or operational context.
• Familiarity with machine‑learning‑based weather forecasting; experience using ML forecasting systems is an advantage.
• Strong programming and scripting skills.
• Demonstrated project management experience, including planning, coordinating, and delivering research activities.
• Excellent communication skills.

EEO Statement

We consider an inclusive environment 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.

Applicants are invited from nationals of ECMWF Member States and Co‑operating States, including Ukrainian nationals.

They are exempt from immigration restrictions if they, or their families, are part of their households.

Location: Reading, UK or Bonn, Germany (Candidates expected to relocate to duty station).

Remote work: up to 10 days/month away from office, up to 80 days/year within the member states.