Data Analyst

Minimum Requirements:

• Bachelors degree in Computer Science, Statistics, Applied Mathematics, or a related field.
• Proficient in Python programming, with experience using data analysis libraries such as Pandas, NumPy, and SciPy.
• Strong expertise in SQL and experience with relational databases.
• Familiarity with Jupyter for interactive data analysis.
• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• Experience in creating visualizations and dashboards using tools like Matplotlib, Seaborn, or similar libraries/frameworks.
• Excellent problem-solving skills and the ability to work independently or as part of a team.
• Effective communication skills, with the ability to translate complex data into actionable insights for non-technical stakeholders.
• Experience with data warehousing and ELT tools.
• Knowledge of machine learning techniques and algorithms.
• Familiarity with version control systems, preferably Git.
• Note that we are a Mac/Linux shop and do not use the Microsoft stack. We use Snowflake for their data lake and Superset for dashboarding.
• Nice to Have: Experience with handling data from market research surveys (SPSS files) and sampling statistics will be useful.

Responsibilities:

• Familiarise yourself with the nature of data that they hold for customers and internally
• Use your knowledge of the business and data entities to construct queries to gather required data using SQL and Python/pandas
• Package the required data in user-friendly spreadsheet and graphical outputs
• Take the lead on automating and delivering regular reporting required by customers and business
• Collaborate with internal teams to understand their data analysis needs and develop reports and dashboards that provide clear insights into key business metrics.
• Work with our data engineer to specify views required to streamline your work.
• Apply statistical methods and data modelling techniques to identify trends, patterns, and insights
• Create and maintain documentation for data analysis processes and decision-making frameworks.