Andreas Bollig

Covered the whole range of data-related tasks at a financial services startup, from ingesting data from different sources into a data warehouse to making in-depth data analyses to support business decisions.

TASKS:
• Refactored existing data ingestion pipelines to allow for code reuse + added pipelines for new data sources + introduced type checking, automated testing, etc.
• Created dashboards for business controlling.
• Created ad-hoc data analyses and discussed their interpretation with stakeholders.

Statistical analysis of online shop click behavior for test products with the goal of making decisions for product portfolio selection and statistical analysis of buying behavior of new customers and customer quality by first product purchased.

I handled the full-stack implementation of features for an existing web application (front end, back end, and database). This included refactoring the existing app and introducing automated tests to the project, as well as adding database management with SQLAlchemy and Alembic.

The task was to develop a solution for identifying geographical hotspots of insurance claims. The claims data was cleaned, geocoded, and geographically clustered. The clustering required the development of a custom algorithm.

The task was to build a sales and operations forecasting system from scratch. Sales forecasts from sales agents needed to be acquired, and gaps needed to be filled with statistical forecasts. The required production capacity in terms of machine hours needed to be derived from the sales forecast.

To provide the controlling department ample time for preparing devaluation numbers for the month-end closing in the context of slow-moving inventory, I built a machine learning solution that forecasts inventory quantities for a big number of materials and makes the data usable via a web-based dashboard.

When bookings in a company's ERP system carry the wrong cost types, the controlling department gets a distorted view of the company's expenses. To this end, I developed a machine learning solution that proposes a cost-type (GL account) for each booking and provides this data to the controlling department via the company's BI system.