Senior Data Scientist - AI for Energy & Markets (Hybrid UK)

Senior Data Scientist - AI for Energy & Markets (Hybrid UK)

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
Talanto

At a Glance

  • Tasks: Develop AI-native capabilities and advanced forecasting models for energy analytics.
  • Company: Wood Mackenzie, a leader in energy analytics with a focus on innovation.
  • Benefits: Hybrid work environment, competitive salary, and opportunities for professional growth.
  • Other info: Work in vibrant Edinburgh or London offices with excellent career advancement potential.
  • Why this job: Join a dynamic team to shape the future of energy with cutting-edge AI technology.
  • Qualifications: Experience in data science, machine learning, and collaboration across teams.

The predicted salary is between 60000 - 80000 Β£ per year.

Wood Mackenzie in energy analytics is seeking a Senior Data Scientist to develop AI-native capabilities and advanced forecasting models, with a focus on cross-domain AI systems and knowledge-graph analytics.

You will build scalable ML models, encode domain knowledge, and collaborate with product, data and engineering teams to deploy AI solutions in a hybrid work environment across Edinburgh or London offices in the United Kingdom.

Senior Data Scientist - AI for Energy & Markets (Hybrid UK) employer: Talanto

Almedia is an exceptional employer that fosters a dynamic and innovative work culture, perfect for those passionate about machine learning and real-time personalization. With a strong emphasis on employee growth, you will have the opportunity to mentor fellow engineers while collaborating with cross-functional teams in the vibrant city of London. The hybrid working arrangements and commitment to impactful projects make Almedia a rewarding place to advance your career in AdTech.

Talanto

Contact Details:

Talanto Recruitment Team

We think you need these skills to ace Senior Data Scientist - AI for Energy & Markets (Hybrid UK)

AI-native capabilities
Advanced forecasting models
Cross-domain AI systems
Knowledge-graph analytics
Scalable ML models
Domain knowledge encoding
Collaboration with product teams