
Data Engineer
Ayşe er dygtig til SAS Enterprise Guide, SAS Enterprise Miner, SAS Forecast Studio, Python, SQL, PL/SQL, Apache Airflow og OpenShift AI og bygger effektive datapipelines og robuste analyseløsninger, der omsætter komplekse data til brugbar indsigt.
Et af hendes vigtigste resultater var udviklingen og implementeringen af kreditscoremodeller til erhvervskunder, herunder den vellykkede oprettelse af PD-scorekort (Probability of Default), der forbedrede risikovurderingen og nøjagtigheden af beslutningstagningen.
Ayşe er anerkendt for at kombinere teknisk ekspertise med strategisk indsigt og for konsekvent at levere datadrevet værdi, der understøtter forretningsvækst og driftseffektivitet.




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.
Alle Data Engineerer, der har ansøgt til Proxify, får en score fra 0 til 300 på teknisk dygtighed, en af de fem parametre, vi evaluerer. Denne score afspejler kun teknisk dygtighed baseret på interviews, hjemmeopgaver, live kodningssessioner og/eller vurderinger af præstationer på jobbet. Kurven viser, hvordan alle evaluerede Data Engineerer er fordelt over dette interval, hvor vores acceptgrænse for denne parameter ligger, og hvor Ayşe står.

Issued Jan 2019
Credential ID DY6YWRSKCJFQ1NW3

Issued Jan 2019
Credential ID DY6YWRSKCJFQ1NW3


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