Weather Data Analyst

4 weeks ago

London, Greater London, United Kingdom Balyasny Asset Management L.P. Full time

Overview

Balyasny Asset Management L.P. is seeking an experienced Weather Data Analyst to work in our London-based Commodities Research and Analytics team. We are building a weather analytics and forecasting platform alongside a geospatial platform to manage scientific weather datasets, provide custom services and advanced analytics for our Commodity investment teams.

As a Data Analyst, you will be contributing to our greenfield platforms by implementing weather analytics, conducting data analysis, building visualisations, communicating weather views and signals and work directly with our Commodity Portfolio Managers and their analysts and traders to help integrate and use weather data in their trading activities. This is a unique opportunity to contribute to a highly impactful initiative by helping to improve our investment teams' ability to predict weather conditions and explore weather related commodity risks.

The ideal candidate will have a technical background in weather and climate research, a strong spatial mindset, with a good understanding on how to apply weather analytics to various commodity markets. They will also have 3-5 years of hands-on technical experience with data driven technologies and be a very strong communicator with the ability to excel in a fast-paced trading environment.

Responsibilities

  • Develop and maintain advanced weather analytics products, focusing on temperature, precipitation, and wind patterns critical to commodity markets
  • Produce and maintain maps, visualisations, and statistical analysis to support custom services.
  • Act as a weather data liaison, collaborating closely with PM teams to enhance their trading strategies through tailored data solutions
  • Synthesize complex weather data into custom indices and actionable insights, leveraging your expertise in geospatial analysis
  • Contribute to the technical build of the platform. Expanding our data access toolkit, address data quality challenges
  • Assist in the identification and prioritization of potential analytics projects and products which could add to business value

Qualifications & Requirements

  • Track record of delivering weather analytics solutions to financial markets
  • Exposure to various weather vendors such as ECMWF, GFS, NOAA, NASA, Commodities Weather Group, and other primary weather vendors
  • Strong foundation in atmospheric science, with experience in building data pipelines from raw climate data in formats such as GRIB, NetCDF, and HDF
  • Proficiency in Python (3-5 years preferred) and familiarity with scientific computing libraries such as Pandas/Geopandas, NumPy, Xarray/Rioxarray, Raterio, Pysal, Pyproj, Shapely, PySpark etc
  • Experience with AWS cloud services and infrastructure, with hands-on experience in data orchestration and deployment (S3, Lambda, Redshift, Glue, ECS, etc.)
  • Proficient in querying data, writing reports and visualizations. StreamLit, Dash or Tableau a plus
  • Exceptional communication skills, with the ability to distill complex technical details into clear, concise narratives for diverse audiences
  • Familiarity with DevOps best practices and tools: e.g., Git, Jenkins, Airflow, TerraForm
  • Degree in Atmospheric Science, Planetary Science, Earth Science, Computer Science, or closely related field

Nice to Have

  • Understanding of various meteorological concepts, a plus for any of the following: numerical weather prediction, sub-seasonal to seasonal predictability, synoptic meteorology, tropical variability, severe weather phenomena
  • Experience working with large geospatial databases, proficiency in QGIS, GeoServer, OGR/GDAL, or similar location analytics tools
  • Machine learning expertise, particularly applied to enhancing weather forecast models
  • Experience with an OOP language such as C++ or Rust
  • Familiarity with commodities and trading. Prior exposure to Commodities a plus

We are looking for a data-driven innovator ready to take on the challenge of predicting the unpredictable.

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