
Data Engineer
Skilled in SAS Enterprise Guide, SAS Enterprise Miner, SAS Forecast Studio, Python, SQL, PL/SQL, Apache Airflow, and OpenShift AI, Ayşe builds efficient data pipelines and robust analytical solutions that translate complex data into actionable insights.
One of her most significant achievements was the development and deployment of credit scoring models for commercial customers, including the successful creation of Probability of Default (PD) scorecards) that enhanced risk assessment and decision-making accuracy.
Ayşe is recognized for combining technical excellence with strategic insight, consistently delivering data-driven value that supports business growth and operational efficiency.




Conducted fraud detection analysis and implemented business rules to identify suspicious transactions;
Applied regression, clustering, decision tree, and neural network algorithms for predictive modeling;
Performed social media analytics and text mining to detect anomalies and patterns in unstructured data;
Utilized SAS Enterprise Guide, SAS Enterprise Miner, and SAS SNA for data modeling and network analysis;
Worked with Base SAS, Python, and R for data manipulation and analytical model development;
Performed fraud detection and implementation of business rules;
Applied regression, cluster analysis, decision tree, and neural network algorithms;
Conducted social media analytics and text mining;
Worked with SAS Enterprise Guide, SAS Enterprise Miner, SAS SNA, Base SAS, Python, and R.

Performed data preparation, cleansing, and analysis to support business intelligence and forecasting initiatives;
Developed short-term and long-term demand forecasting models using statistical and machine learning techniques;
Conducted descriptive analysis, time series modeling, cluster analysis, and decision tree analysis to uncover business insights;
Applied neural network algorithms to improve prediction accuracy;
Scheduled automated reporting and analytics jobs;
Utilized SAS Enterprise Guide, SAS Enterprise Miner, and SAS Forecast Studio, along with Base SAS and Python for model development and reporting;
Performed data preparation, data cleansing, and data analysis;
Developed short-term and long-term demand forecasting models;
Applied descriptive analysis, time series analysis, cluster analysis, decision tree analysis, and neural network applications;
Managed scheduling of jobs and reporting;
Worked with SAS Enterprise Guide, SAS Enterprise Miner, SAS Forecast Studio, Base SAS, and Python.
Worked with SAS Enterprise Guide, SAS Enterprise Miner, and SAS Forecast Studio;
Utilized SAS Social Media Analytics, SAS Text Miner, and SAS Visual Analytics.
All Data Engineers who have applied to Proxify are scored from 0 to 300 on engineering excellence, one of the five parameters we evaluate. This score reflects engineering excellence only, based on interviews, take-home assignments, live coding sessions, and/or on-the-job performance reviews. The curve shows how all evaluated Data Engineers are distributed across that range, where our acceptance threshold for this parameter sits, and where Ayşe stands.

Issued Jan 2019
Credential ID DY6YWRSKCJFQ1NW3

Issued Jan 2019
Credential ID DY6YWRSKCJFQ1NW3


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