Data Science Analyst

As a Data Science Analyst, you will work with analytics colleagues, software engineers and architects, Soho and clients to carry out complex analyses, build statistical and machine learning models, and develop actionable recommendations for a wide-range of strategic topics in industries such as financial services, real estate, retail and public sector.

Key Skills:

  • Mathematics:Mathematics is the foundation of data science, and data scientists need to have strong math skills. They use these skills to interpret data, create formulas and models and perform calculations. They also use math to create visualizations of data.
  • Computer programming languages:Data scientists need to know at least one computer programming language, such as Python, R or Java. They use these skills to create scripts and programs to analyze data and create visualizations.
  • Data analysis:Data analysis is the process by which data scientists interpret data and draw conclusions from it. Data analysis requires a combination of technical skills and business knowledge. Data scientists need to understand the data they’re analyzing and the questions they’re trying to answer with it.
  • Business skills:Data scientists need business skills to communicate with business stakeholders and explain the value of their work. They also need business acumen to understand the financial implications of their work and the potential return on investment of data science projects.
  • Communication:Data scientists often work with other data scientists, engineers, marketing teams, management and other stakeholders. Effective communication is crucial to the success of a data science project. Data scientists should be able to explain complex technical concepts in a way that non-technical people can understand. They should also be able to clearly explain their data analysis and results to other data scientists.


Key Responsibilities:

  • Working with other members of the team to develop new models or improve existing ones
  • Creating reports that present results of analyses in a way that is easy to understand
  • Communicating effectively with team members and stakeholders to ensure that all parties are on the same page
  • Conducting analyses to identify trends or patterns in data sets
  • Developing and implementing new algorithms to improve existing models
  • Conducting research to identify new opportunities and challenges within an industry or market segment
  • Developing computer models that can be used to predict consumer behavior
  • Finding relevant data sets that can be used to test hypotheses or support theories
  • Identifying research questions that can be answered using data analysis methods such as statistical modeling
  • Interpreting findings and communicating them in a way that is easy for non-experts to understand

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