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Sarah Silva

Freelancer

Data Analyst

London, United Kingdom

Data Analyst

I've worked in data analysis for 9 years, for local agencies that need clarity from scattered spreadsheets and CRM exports. I work in Python (pandas) for processing and Power BI or Google Data Studio for visualization, chosen based on what the client can maintain themselves. I start from the decision the business needs to make, not from the chart. I don't build machine learning models — that's a related but separate specialization. A typical project is an audit plus one working dashboard, five to seven working days.

Data Analyst

Skills

PythonPandasPower BIGoogle Data StudioSQLDashboards

Portfolio

Retail chain sales dashboard — Dashboard

Dashboard — Power BI dashboard connected to CRM data, sales trends by location.

Subscription unit economics — Financial analytics

Financial analytics — Calculated LTV, CAC, and break-even from six months of subscription data.

User retention cohort analysis — Retention by signup cohort for a mobile app

Financial analytics — Retention by signup cohort for a mobile app, visualized month over month.

Retail chain sales dashboard — Power BI dashboard connected to CRM data, sales

Dashboard — Power BI dashboard connected to CRM data, sales trends by location. A second, similar engagement for a different client.

Subscription unit economics — Calculated LTV, CAC, and break-even from six

Financial analytics — Calculated LTV, CAC, and break-even from six months of subscription data. A second, similar engagement for a different client.

User retention cohort analysis — Data Analyst

Financial analytics — Retention by signup cohort for a mobile app, visualized month over month. A second, similar engagement for a different client.

Retail chain sales dashboard — Data Analyst

Dashboard — Power BI dashboard connected to CRM data, sales trends by location. A follow-up piece built on the same approach.

Subscription unit economics — Data Analyst

Financial analytics — Calculated LTV, CAC, and break-even from six months of subscription data. A follow-up piece built on the same approach.