Richard Nemeth Email & Phone Number

Copenhagen, Dk

Copenhagen, Capital Region, Denmark

Copenhagen, Capital Region, Denmark

Copenhagen Area, Capital Region, Denmark

Copenhagen Area, Capital Region, Denmark

Ballerup, Capital Region, Denmark

Copenhagen Area, Capital Region, Denmark

Building sustainable & long-term analytical solutions.

Copenhagen Area, Denmark

Data Scientist position in the Merchant Services area, Customer Intelligence and Campaigns.Key activites:- Retention: Building proactive retention ML model and its productionization.- Data engineering: Support of sustainable data flows and their execution, support in design and architecture of data flows on Hadoop- Data extracts: Ad-hoc data extracts/data analyses to provide answer(s) to the business unit- Setting up a generic data science environment, which allows data scientists to build models and ML products in a productionized fashionTools:- Python, R, Spark, Hive

Copenhagen Area, Capital Region, Denmark

Data scientist position in the Global Analytics department with main focus on big data platforms and their usage in Data Science area for business purposes.Key projects: - Fraud: Acquisition and cleaning of the source data for ML purposes, data preparation, data wrangling, building ML models, productionization of ML models for real-time scoring, optimization of ML models and data science processes, automization of DS processes (offline champion-challenger setup), investigation, visualization and skeptical ways of evaluation of both models and results, continuous communication and alignment with the stakeholders and business units-AMLTool package:- Big data: HDFS, Hadoop, Spark, Hive (Beeline)- Programming: Scala, Python- DS: Python, JupyterResearch areas:- Bayesian optimization

Copenhagen Area, Capital Region, Denmark

A collaboration between University of Copenhagen and Danske Bank with focus on credit card fraud detection, based on standard and more advanced machine learning algorithms. The output should be a feasible combination of models, which in combination accurately predict the probability of fraud transaction-wise, including the explanation of the score and the models should be applicable to production if needed.

Copenhagen Area, Denmark

Student data scientist positionKey activities: - big data work: including data analysis, data moving, data cleaning, sanity checking, data quality assurance, data transformations - feature engineering; spark, scala, python, R - modelling: imbalanced problem modelling, classification and regression models, model evaluation, model performance; spark, scala, python, R, - production: real-time analytics, productionizing models; python, scala - R&D: innovative modelling techniques, model evaluation research, Spark and Python development - communication and cooperation; LaTeX, TeXMain projects: - tools investigation - fraud detection - deep learningTools and platform: - Apache Hadoop : hive, spark (scala), ambari, yarn - SQL - Python - Scala, Spark, R - statistical software: R, Wolfram Mathematica, Matlab - version control: git - documentation: LaTeX, TeX

Prague, The Capital, Czech Republic

Acquisition of new clientele, consulting, complex analysis of needs and matching expectations, customer service, retail

Master’S Degree, Actuarial Mathematics

Master thesis: Credit card fraud modelling

Bachelor’S Degree, General Mathematics

Specialisation: Financial and Actuarial Mathematics, Mathematical Statistics Thesis: Pearson's correlation coefficient and its use.