
Software Engineer
Job Summary
As a Data Scientist, you will work within a cross-functional agile team contributing to the company's growth in a fast-moving environment. You will develop new tools and features by working closely with engineers, product managers, designers, and engineers. Ultimately, the goal is to help our users make smarter decisions. On top of that, you’ll have the support of a diverse, large team of data scientists working across all Brandwatch products to support your technical and career development. This role is open to people with a variety of professional and academic backgrounds and you can expect to develop your skills in a wide variety of techniques, including machine learning, statistical modelling, and computational linguistics.
Essential Duties and Responsibilities
Work with stakeholders to clarity and refine questions.
Collect, clean, and prepare data, generally this will be large volumes of text along with metadata and will often need to be handled with big data tools.
Build models to solve a range of different problems, such as text classification and clustering, automated insight generation, and time series analysis.
Develop evaluation methodologies, which will vary from those based around conventional classification metrics (such as precision and recall), to designing methods to evaluate how well your tool helps a user achieve a defined task.
Support engineers as your projects are shipped. Sometimes you will also help maintain and upgrade the tools and technologies you develop.
Perform other duties as required to support evolving business needs.
Minimum Required Qualifications
A good working knowledge of Python (or another modern programming language, with willingness to learn Python).
A solid understanding of statistics and/or machine learning.
Enjoy an experimental and iterative approach to building products.
An ability to communicate with non-technical stakeholders, or willingness to skill up in this area.
Preferred Qualifications
Working in a data science team.
Common data science libraries, such as Numpy, Scipy, Pandas, SKlearn, Spacy.
Big data tools, such as: Spark, Dask, MapReduce, or alternatives.
Cloud computing, for example: AWS EC2, S3.
Linux style shell commands.
Application prototyping frameworks such as Dash, or Shiny.
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