André Michel Bimbai

Achievements:

• Analyze data to answer business questions

• Implementation of data infrastructures

• Development, implementation, and maintenance of databases

• Data quality assessment

• Generate insights and ideas from datasets and identify trends and patterns

• Prepare reports for management and project teams

• Dashboard development

Technical skills: Google Cloud Platform (BigQuery), PowerBI, SQL, Data Build Tool (DBT)

Achievements:

• Development of automated reporting for monitoring automatic recovery strategies:

• Identification and extraction of relevant data from various available sources (Snowflake, Medium)

• Processing and transformation of data into a single table of useful variables for monitoring recovery strategies

• Creation of dashboards (tables and graphs) for monitoring recovery strategies

• Construction of KPIs for monitoring recidivism on unpaid debts

• Production of monthly reports

• Detailed documented procedures.

Technical environment: SAS, SQL, Excel, Microstrategy, Snowflake

Achievements:

• Data processing: Perform quality checks on data sources to verify missing data, inconsistencies, and carry out data processing and transformation activities into variables useful for analysis.

• Data analysis: Ensure statistical analysis methods according to the project

• Descriptive and inferential statistics: collect observations on subjects, translate these observations into numbers that provide information on certain properties (frequency, percentage, mean ± standard deviation, hypothesis testing, correlation) and describe the summarized information in tables and figures.

• Survival analysis: In the context of longitudinal studies or clinical trials, estimate the distribution of survival times (survival function), compare the survival functions of several groups and how explanatory variables modify survival functions using Cox regression and competing risks methods.

• Predictive and/or prognostic analyses: In the context of longitudinal studies or clinical trials, develop (or train) models to predict the probability of the target variable based on the estimated weight of a set of input variables. If applicable, depending on the study's objective, evaluate the trained model on a validation sample (Logistic regression, propensity score and penalized regression, etc.).

• Data reporting: Monitor service activity Ensure the production of performance indicators and the dissemination of service activity reports (tables and figures) Detailed documented procedures.

Technical environment: SAS, SQL, Python, Tableau, Excel