The Merchant Success Department is seeking a detail-oriented Data Analyst to support our mission of enhancing customer satisfaction and retention.
The Data Analyst will work closely with the Customer Success team to provide insights into customer behavior, identify trends, and drive data-informed strategies that improve the overall customer experience.
Responsibilities:
- Identify and acquire data from different data sources (production, warehouse, external sources) and organize the data in usable formats.
- Develop and implement data collection systems, pipelines, data analytics and other strategies that optimize statistical efficiency and quality.
- Interpret data, analyze results using statistical techniques, create visualizations of data and provide reports, and dashboards.
- Identify, analyze, and interpret trends or patterns in complex data sets.
- Use BI tools to analyze data, and extract useful business insights.
- Filter and “clean” data by reviewing reports, dashboards, and performance indicators to locate and correct problems
- Locate and define new process improvement opportunities.
- Supporting business departments and responding to their data-related requests and keeping track of these requests.
- Supporting data engineers in identifying and revising reporting requirements.
- Supporting initiatives for data integrity and normalization.
- Training end-users on new reports and dashboards.
- Work with management to prioritize business and information needs.
Requirements
- Bachelor's degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.).
- Minumum two years of experience as a Data Analyst or Business Data Analyst.
- Proficiency in Python (other languages such as Shell Scripts, C++, .Net, Java, Matlab, R,...etc are preferred).
- Excellent in Algorithms and Data Mining techniques.
- Familiarity with database technology (design and implementation) and query language.
- Strong knowledge of and experience with BI tools (e.g. Metabase, Looker, Tableau..etc) and reporting packages.
- Knowledge of statistics and experience using statistical packages and python libraries for analyzing and visualizing datasets (NumPy, SciPy, Tensorflow, Keras, Pandas, Sklearn, MatplotLib... etc).
- Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
- Adept at queries, report writing and presenting findings.
- Strong organizational and project management skills.
