Data Analyst

Introduction:

The candidate has a strong background in data analysis, data science, and project management across diverse industries. They are highly skilled in data cleaning, data mining, and quantitative/qualitative analysis using Python, SQL, and various BI tools. The candidate has experience working with large datasets, performing exploratory, diagnostic, and inferential analysis to assess and improve machine learning models. They are adept at identifying data quality issues and collaborating with cross-functional teams to enhance data curation and model performance. The candidate has built and presented insightful reports to stakeholders and has a proven track record of improving business performance through data-driven decisions. They also have expertise in creating and managing dashboards, KPIs, and financial reporting. Their work spans across supporting marketing and sales teams, automating manual processes, and enhancing customer experience through data enrichment. The candidate has a solid understanding of project management, having supported financial forecasting, P&L analysis, and process improvement initiatives. They are also experienced in managing both large and small teams, with responsibilities in stakeholder management, process creation, and continuous improvement.

Responsibilities:

• Perform in-depth analysis on large datasets to assess and improve machine learning models.
• Identify data quality issues and collaborate with technology teams on data curation and cleaning.
• Build, maintain, and present reports and insights to stakeholders, suggesting improvements to drive better model performance and customer experience.
• Create and track KPIs, and develop dashboards to monitor business performance.
• Perform quantitative and qualitative analysis to generate actionable insights from sales data.
• Improve sales pipeline visibility by developing marketing KPIs and dashboards.
• Track financial KPIs and build monthly/quarterly financial reports.
• Optimize CRM tools, replacing manual processes with automated API queries.
• Write reusable code to support future analytics projects and conduct unit testing.
• Maintain dashboards to track data quality and customer experience improvements.
• Use web scraping tools and regular expressions (RegEx, XPath) for data collection and cleaning.
• Review the accuracy of data sets across the data pipeline, ensuring data consistency from ingestion to release.
• Automate manual processes to improve team effectiveness and operational efficiency.
• Support the bidding process and assist with creating commercial offers for clients.
• Analyze financial KPIs, perform P&L analysis, and support financial forecasting and variance analysis.
• Manage project reports, dashboards, and metrics to track project progress and financials.
• Provide training and documentation to new team members on IT service processes and tools.