Lead Analytics Developer : Python : Global Energy Company
A Leading Energy company has an exciting opportunity for a Lead Analytics Developer to work on Cross Desk Data Ingestion & Analytics, leveraging data for visualisations and analytics.
They are seeking self-service enablement – a team who can work with people on their self-service data and analytics journey – provide advice, templates, design advice. Based on the principle that “self-service”/DIY development will not be possible completely independently – they will need some IT support to build that new skill set and capability within the commercial teams and pilot a framework for quant model deployment
The successful candidate will need:
– Python e.g. Packages: Numpy, SciPy, Sickt-Learn or Keras or Tensorflow
– Experience working with PowerBI – its capabilities, limitations etc.
– Power Trading/Commodities Experience
– Statistical background e.g. Masters or PhD in STEM Subjects, (Financial Engineering preferable)
– Experience in working with low granularity time series data
– Experience in working with Data Engineers to get the most out of ETL processes
– Experience with Azure (Blobstorage, Cosmo DB)
– Dedicated to Agile best practices – (shared understanding, enough documentation, focus)
– Familiarity with storage quality practices – (Gold/Silver/Bronze)
– Experience establishing development best practices within a complex and fast paced environment – CI/CD, testing, container management, SCA etc.
– Experience designing and developing distributed applications such as liner solvers and modelling – the exact technology stack is less important than an involvement in designing the approach
– Strong experience with Azure components and infrastructure management (devops)
– Must be willing and capable of mentoring and developing others
– Experience with fundamental modelling of commodities, ideally energy (power, gas, environmental products), and with meterology, would make the top candidates stand out
The Lead Analytics Developer will build best in class fundamental market analysis across all geographies and time horizons to underpin decision making. This team aims to improve the data available for trading analytics to support decision making.
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