Fachlicher Schwerpunkt dieses Freiberuflers

Senior SAS Data Scientist. Multiple Certified. Advanced Analytics (prescriptive/predictive); Data Mining; Enterprise Reporting; BASE etc. Programming.1

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  • Certified SAS Professional (Miner, Analyst, Programmer)
  • Data Scientist
  • Scientific Consultant


05/2019 - 12/2019

8 Monate

Analysis, optimization and forecasting of manufacturing data

Data Analyst (SAS JMP)
Word-leading glass manufacturer
SG, Switzerland
  • Supporting the analysis, optimization and forecasting of data of processes, equipment and products with statistical methods, programmed in JMP JSL. Developed prototypes from various sources based on approaches like SPC, 6Sigma, Data Mining, Visual Analytics, and Event Forensics. Supported the design and automation of routine analyses (JMP), reports (FabEagle) or dashboards (CroNet) for enterprise-wide process development and production. Visualized data in an intuitive way, e.g. production line aligned heatmaps or interactive parallel coordinate plots. Analytics included target analytics and analysis of association production-wide KPIs. Data Mining included the automatic picking of the (e.g.) “Top Ten” of most instable process KPIs. Managed and coordinated projects for data analysis and acted as a sub-project manager in specialized projects, e.g. AQL. [Job description in progress]

Data Mining


Data Analysis


Event Forensics

Visual Analytics


People Management





Windows 10


JMP 15.0/14.0

04/2018 - 03/2019

1 Jahr

BCBS 239

SAS Team Lead
Leading credit lending bank
  • Transformation of dozens of individual reports (some even manually or Excel-based) into an auto-matic bank-wide SAS-based management reporting system according to BCBS 239 standards. Re-sponsibilities include SAS Team Lead, designing of reports (corporate design, banking reporting standards), mapping of input data sources and SAS specs with required report outputs, definition and tracking of complex ETL processes in-between including interfacing with in-house experts. Extensive SAS programming and issue solving. Thorough testing of technical quality and subject matter accuracy of data supplied, including bugfixing, documentation, and communication. Using a central Visio-based workshop to establish a required mutual understanding and wording of a highly complex proprietary data processing environment (“SAS Batch Framework”).

Windows 10.

EG7.1 (SAS9.4




SAS Base

SAS Macro Facility


“SAS Batch Framework”

01/2018 - 03/2018

3 Monate

Project "MonSter"

SAS Consultant
Leading German car and liability insurance
  • Migration of a nonperforming propriety Excel application to a high-performing SAS macro. Auto-mation of various data imports [incl. EMESSO, STORNO, INCASSO]. Replication of processes and calculations of original Excel analyses by SAS code and macros. During the migration, Dr. Schendera carried out further optimizations: Conversion from mathemat-ical to calendar day calculations; implementation of special calculations including maturity, previ-ous month deltas, etc.; eliminating duplicates from original Excel data stores; automatic channel-specific distribution of the reports (email); standardization of address data for marketing mailings.


  • Alignment and consistency of application-wide meta-data. Results replicated by the new application were externally verified and correct without exception. Generating user-friendly reports: Designs depending on the specific report with its own background and traffic-lighting of business-relevant values. Despite a significantly higher functional range, e.g. extended by further reports and func-tionalities, the original processing time reduced from 9 hours to 12 minutes. Reduction of this pow-erful SAS application to a user-friendly cockpit with just a few "switches", including report requests, data screenings, and options for data updates.

EG7.1 (SAS9.3)

SAS Base

SAS Macro Facility



10/2017 - 12/2017

3 Monate

Replication & documentation of a "black box application"

SAS Risk Consultant
Leading online marketplace for peer-to-peer loans
  • Development of various reports with SAS. Replication and documentation of a "black box application" (third party). Extensive verification of the quality of manually created third-party data deliveries. Create reports as self-service visualization or analysis in Sisense on this data basis. Activity mainly in the area of receivables management (Forderungsmanagement), i.a. Dunning levels, changes in installment plans, CC1-CC5 status ("file overview"), effectiveness of outbound telephony ("promises"), etc.


  • Depending on the degree of standardization of the data source (semi)automation of the original manual steps of reading in and analyzing the data, including extensive verification of the technical quality and subject matter accuracy of data supplied by third parties. Replication of a "black box application" (third party), originally written in ORACLE PL / SQL using SAS Base and PROC SQL. Visualization and documentation of the validated SAS version (replacement of the "black box") i.a. using entity-relations diagrams, drawn with draw.io. Receivables management reports were automatically provided as data for display in Sisense and as ready-to-use EXCEL files with heavy traffic-lighting of relevant figures. Reports satisfied management demand and audit standards. In case of black box, now control over application’s ETL instead of black box vendors having control over client. Reduced processing time, from down hours to seconds, less error-prone.

Windows 10

EG 7.15

DBeaver 4.2.3

Sisense 6.7 (incl. Elasticube Mgr)

draw.io (Confluence)


SAS Base

SAS Macro Facility





04/2017 - 08/2017

5 Monate

Data analysis "Project Change Forecast"

Data Scientist
Leading German supermarket chain
Cologne area
  • Data analysis "Project Change Forecast", expert advice on the feasibility of the case study, statistical analysis of real-time data, as well as workshops and presentation of the results.


  • Successful design of a cash order monitoring system for two known German retailers of an international holding company based in Cologne, Germany. Developed SAS solution allows views on the same order process from different analysis angles (visual and statistical). Management approval to implement this case approach Europe-wide. Case study allows each market to order cash while controlling relevant costs factors of the cash-delivery system (coin weight, transportation). Expected savings: Millions € p.a. on business case alone.

Data and approach:

  • Daily change orders of several hundred retail markets over two years. Comparative analysis using Tine Series, Operations Research, and Statistical Process Control approaches. Development and discussion of several scenarios, e.g. one-time and repeated standard order suggestions. Robust approach from data source to dashboards and EXCEL sheets incl. traffic-lighting of relevant results. Different time-horizons (month, quarter, year), as well as different prediction precision intervals. Hybrid approach bridging Statistical Process Control and Time Series Analysis incl. solutions for business vs. calendar week dilemmas.



SAS Base




SAS Macro Facility


10/2014 - 06/2017

2 Jahre 9 Monate

Business logic for monitoring cancelling brokers

SAS Program Manager, Project Manager Data Mining
Major insurance company
Cologne/Düsseldorf area
  • Developing a business logic for monitoring cancelling brokers: As soon as brokers announce their intention to cancel, the system initiates timely action to prevent the customer relationship from shifting to a competitor. Business


  • 900+ Million € p.a. Designed and implemented a future monetary risk warning system that facilitates sales force to identify and target high-value customers in time before leaving. KPI approach. Business Impact: 100+ millions € per annum. Implemented and extended several projects computing purchase probabilities. Data mining approach. Business case alone generated 12+ million € (wins). Several marketing campaigns confirmed the reliability of these models and estimates.


  • Support management and team with advanced data management, data mining / analysis skills, adapt to fast-changing requirements and multi-task in a fast-paced insurance environment.

Tasks and Achievements:

  • (1) KPIs for Future Monetary Risk Warning System: Identifies and ranks potential churners according to overall value, and time left for sales force to respond. One special feature presents automatically Top 5 high-value customers to sales force including data for immediate contact. (2) Data Mining: (i.) Elected member of team to design and implement a early warning system of lapse or persistency risk of customers (Stornofrühwarnsystem) based on SAS EM and ABTs. (ii.) Identification and analysis of Sales Force Agents who intend to churn. Calculation of financial risk parameters, quality of customer care, fraud indicators, as well as probability of client churn probability. (iii.) Migration of third-party EM projects. Implementation and automation of a fully automated EM/EG data mining approach to forecast the purchase of specific insurance products. Extended from yearly to monthly forecast. (iv.) Back-testing of predicted sales values to actual occurring events in proof of concept (e.g. by ASEs, ROCs, binning and lift charts). Transformation of PoC results into Business Case. Computation of several KPIs incl. added wins and saved investment. Risk analysis of various scenarios of sustainability of customer investment. BC achieved GO from top management. Developed several models totaling up to 12.5 millions € (wins), or 5 millions € (savings). (2) Reverse engineering of strategic business reporting system for sales force: Porting (migration) of dysfunctional ETL and analysis programs (SPSS) into a high-performance SAS version, while debugging, tuning and enhancing on-the-fly. (3) Analysis of bonus program for preferred insurance portfolios on behalf of Concern Development, e.g. ABC, top client, insurance classes, and on organizational level (unit, distribution channels, prior year). (4) Fraud detection: Identify fraudulent sales force by pattern analysis of terminating old and procuring new contracts, special pattern analysis of contract shifting, and identifying fake address data. (5) Evaluation of the on-site Customer Contact Management by sampling, ETL, and export of contact data of cases and controls to host (TSO), SAS and EXCEL according to specification. (6) Selection by advanced random sampling of customer data according to specifications for a partner mailing incl. technical and statistical documentation. (7) Others: (i.) Migration of SAS Code and EG Projects from SAS9.3 to SAS9.4 (ii.) Multi-layered random-based anonymization of SAS datasets. (iii.) Deduplication projects, e.g. of email address database. (iv.) Phonetics-based merging of customer data from multiple SAS sources using a Fuzzy Match. (v.) Implementation of top client key in host (TSO) and in all end systems including communication, test, acceptance and documentation of the various organizational, technical and personal interfaces. (vi.) Transfer of functionalities of Enterprise Guide/Miner projects into SAS batch code, and vice versa.

Complex. Host (z/OS)

Citrix XenApp


SAS Base

SAS Macro Facility




Enterprise Guide 7.1/5.1

Enterprise Miner 14.1/12.1

SPSS 24/19





05/2014 - 08/2014

4 Monate

Designing an Issue Map

SAS Project Manager (Programmer, Analyst, Consultant)
Major insurance company
Munich area
  • Designing an Issue Map pinpointing threats and risks (even unknown before) in the client’s system. Condensing complex information down to concise interim results presented to higher management.


  • “Task Force”. From System Analysis to Management Consulting. It came to mgmt attention that a selected process in enterprise-wide system lost more and more performance, reliability and stability. Initial priority responsibility was to analyze possible causes; at first limited to analyzing Korn shell scripts, SAS programs and macros of this selected process. Analysis showed that the selected process was only a symptom, not the root cause. Role extended to gather information about causes possibly threatening the whole system. Approaches involved identification and communication with stakeholders, gather and validate information, designing an Issue Map pinpointing threats and risks in the client’s system, and condensing complex information down to concise interim results presented to higher management. Methods applied were Business Analysis (Stakeholders, Data Flows, OEs, etc.), Downtime Threat/Impact Analysis, Risk Analysis, Architectural Analysis, and Cost Estimates for Project Mgmt tackling identified issues for quick-wins. Responsibility shifted again to develop technical concept, road map, and project plan to stabilize the whole enterprise-wide system by also proposing a grand solution with sustainability provided by state-of-the-art hardware, software, and computing.


  • Positive feedback for doing a multi-faceted job, solving some issues “on the fly”, successful identifying threats (even unknown before), and communicating professionalism, perspectives, and confidence. Short-term emergency contract was extended several times.

Complex. SAS v9.1.3 in programs

DB2 on host

files in EBCDIC and other formats

several systems for scheduling and transferring (UC4 etc.)

MS Project v2013

BIP (Batch Import Procedure) Tool v1.1.4

Alerting and Monitoring (AMT) Tool v1.6 (AZD)

03/2014 - 08/2014

6 Monate

Developing several SAS programs

SAS Statistical Programmer
Pharma Intelligence Company
Frankfurt area
  • Developing several SAS programs for automatic statistical analysis of diabetes data.


  • Developing, testing and running SAS programs for statistical analysis of pharmaceutical data from the diabetes area. Scientific consultancy includes statistical analysis, development and testing of complex ETL routines to access, process, and analyse data from different sources and formats, diabetes-related input from studies monitored in the past for international meetings, monitoring of quality of third-party data deliveries including intervention to protect client from dirty data, evaluation of third-party SAS program performance, and if necessary trouble-shooting and tuning of these SAS programs accordingly.


  • Professional SAS programming and program management just by informal call and also developing a professional IT manual. SAS programming was done according SOP BIO5 (KKS), validation of the SAS programs according to SOP BIO6 (KKS). Due to satisfaction, customer extended short-term contract several times.

SAS Base

SAS Macro Facility and PROC SQL

SAS v9.3 on a Windows environment

04/2014 - 04/2014

1 Monat

Developing & testing of jobs and subjobs

SAS Programmer
Major Bank
Düsseldorf area
  • Developing and testing of jobs and subjobs for ETL on SAS Data Integration Studio 4.3. Writing and testing SAS macros.

DIS 4.3

Toad 5.0



Citrix Environment

02/2013 - 04/2013

3 Monate

Project “Fusion”

SAS Statistical Programmer
Bayern, Munich
  • Results successfully validated and presented to BAFin.


  • Project “Fusion” (working title). Developing and testing SAS macros for the comparison of tariffs of two acquisitioned and merged insurance companies (BK, UKB). Programming and testing of complex ETL processes for analyses of development of insurance tariffs over time, probability to change insurance, and comparison of tariffs by gender, tariff, selected age, number of tariffs, average damage and complex other insurance-relevant requirements. Analysis involved so-called “Steuertabellen” (control tables): Importing control tables, triggering complex ETL processes and analysis (nested loops), and exporting SAS results into user-defined EXCEL sheets on a fingertip. Programming with SAS Macro Facility and PROC SQL.


  • Successful finishing a project without any job description, just by informal request, in a tight time frame (working hours included several weekends).

11/2012 - 01/2013

3 Monate

Developed the Zensus 2011 statistical algorithm

SAS Statistical Programmer, Statistical Analyst
Zensus 2011, IT.NRW
  • Developed the Zensus 2011 statistical algorithm as powerful, yet user-friendly SAS macros.


  • Adjustment of cells to projected margins for 1,440 communities in 65 model variants (volume: 5.5+ billion data rows), a total of 93,600 models and visualization of the numerous GoF parameters. The remaining procedure followed Bishop, Fienberg and Holland (2007) model pre-selection by comparing the estimated table reference table based on AIC, Pearson chi2 and log-likelihood for the log-linear models 1 to 65 Model fine selection based on minimal deviation (deviance) of the pre-selected models of the cell cast reference table (combinations of age, nationality, marital status and gender), additionally taking into account the size of the community to the exclusion of estimation errors and possibly disproportionate cell frequencies. Application combines numerous functions in a single ETL module that can be run as a SAS Stored Process as a unsupervised macro. This complex ETL module consists of two functionally disparate phases: In the first phase, PROC IML (CALL IPF) iteratively provides the data tables for each municipality for each of the 65 models. The second phase iteratively calculated GoF tests, essential parameters were aggregated and formatted as SAS data sets. In addition, this macro specifies criteria for (un)successful convergence (e.g. Chi2, maximum difference, N iterations), as well as pre-set stop criteria (maximum difference, maximum iterations) into a separate SAS file. On top, development of a module focussing on delivering intuitively interpretable visual analysis of GoF and deviance values. A "cockpit" with various "switches" allows fine-tuning the preferred visualization, as well as the input (local, state, all).

Enterprise Guide v4

back-end SAS 9.2 servers

directly or via CITRIX

SAS data sets

SAS Macro Facility

Base SAS


SAS hash Programming

7 PROC SQL and SAS procedures MEANS


04/2011 - 08/2011

5 Monate

Concept for a Quality Intelligence System

SAS Analyst, Project Manager
Wincor Nixdorf
  • Exact and timely identification and feedback of faulty and correct production data. Estimation of data accuracy so far: 90+%. Successful delivery within time and budget, also topping quality expectations. Motivated even reluctant stakeholders to cooperate in project.


  • Concept for a Quality Intelligence System (QIS) for pooling, analysis and visualization of production data to distributed enterprise-wide dashboards in real time. QIS supports the monitoring of product and production quality, and also the prediction of deviations to intervene quicker, as well as a Knowledge Base for improved optimization. Presentation to decision-makers. Demonstrations by ad hoc analysis of production data using SAS products based on classical Six Sigma and more sophisticated predictive analytics approaches. Transformed QIS concept into project plan, defined work packages, coordinated with decision-makers. Interface between i.a. manufacturing, quality assurance, production support, and C levels.


  • Communication of (dis)advantages of various software systems, statistical approaches, meaning and limits of results obtained (including forecasting, decision trees, or various control charts and KPIs).

12/2010 - 03/2011

4 Monate

"Integrated Consumer Services"

Interim Project Manager, SAS Programmer, Statistical Analyst
Santander Consumer Bank (ISBAN DE)
  • As Interim Mgr, I lead team through several obstacles, after joining team I contributed in solving data issues.


  • Temporary lead of the ICS "Integrated Consumer Services" project team (N=8). Central bundling, query and analysis of customer data for marketing activities and credit decisions. Evaluating customers credit applications at POS for fraud prevention. Modeling of credit risk. LGD, EAD and PD (component model). For Basel II audits also integration of daily data updates in a hitherto monthly updated database. Other tasks: Error analysis, log analysis, gap analysis, coordina-tion of technical interfaces (SAS, ORACLE), SLAs for reliable data deployment, (re)programming with SAS Macro Facility, analysis of performance and efficiency (architecture, SAS code, SAS Soft-ware Solutions as an alternative to proprietary SAS code), hardware / software cost estimates for high-performance 24 /7 availability. Data volume: 15+ million data lines.


  • Successful deployment a timely updated single view on accounts, debits and fraud potential of customers in Basel II context. Successful leading of team through phase of unexpected personnel changes. Provided team with access to unprocessed raw data for trouble-shooting while successfully managing data security issues. Step-by-step solving massive data problems caused by third data-delivering departments. Collecting information about processes into documentation (none available until then).


Projekt Vorhersagemodelle zu Mobilfunk-Vertragsverlängerungen, 03/06 - 06/06
Referenz durch Telekommunikationsunternehmen (>3.000 MA) vom 06.11.06

"[...] Der Research Analyst hat seine Aufgaben auf der Grundlage eines umfassenden Sachwissens engagiert zu unserer vollen Zufriedenheit durchgeführt."


SAS Base
SAS Macro Facility
SAS Syntax
SPSS Syntax

Produkte / Standards / Erfahrungen
Data Analysis
Data Mining
Event Forensics
JMP 15.0/14.0
Visual Analytics

25+ years of experience in applied statistics and research methods. Unique mix of Advanced Analytics, Programming Skills, SAS, and Management Experience, and a still hungry attitude to improve data-driven knowledge and decisions.


Advanced Analytics, Data Mining, Data/Business Process Analysis/Reengineering, Reporting.


Insurance, Finance, Banking, Credit, Marketing, Manufacturing, and others.


Knowledge Construction by Statistics, SAS/SPSS and other professional Approaches.


Processes, People, Projects, Products.


Business / Decision Intelligence, Leading by Analytics, ETL and Data Quality.

Preferred systems:

SAS incl. Programming (BASE, SQL, STAT, Macro etc.), and JMP and SPSS.

SAS Platforms:
SAS 9.4 (x64 based) back to SAS 6.08. Windows and others. SAS Viya.

SAS Clients, Interfaces, and Technologies:
Enterprise Miner 15.1, Enterprise Guide 7.1-2, SAS Studio 5.2-3.8, Data Integration Studio 4.3, JMP 15-5, SAS BI Dashboards, SAS Information Delivery Portal, SAS Information Map Studio, SAS Display Manager, ODS, Analyst, Insight, etc.

SAS Modules for Programming and Analysis:
For example: Base, ETS, GRAPH, IML, OR, QC, STAT.

SAS Languages:
Base, PROC SQL, FEDSQL, CAS Programming, Macro, DS2, Annotate, Hash Programming etc.

Data Mining: Structured data
Applied Analytics Using SAS Enterprise Miner:

Skills to assemble analysis flow diagrams using the pat-tern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).

Advanced Analytics in a Big Data World:

Applying and monitoring analytical models like: Decision Trees, Ensemble Methods, Rule Representation; Neural Networks; Support Vector Machines (SVMs); Bayesian Networks; Survival Analysis; Social Networks. Fraud Detection Using Descriptive, Predictive, and Social Network Analytics: Supervised learning (using a labeled data set), unsupervised learning (using an unlabeled data set), and social network learning (using a networked data set).

Data Mining Techniques:

Predictive Analytics on Big Data: Applications and techniques for assaying and modeling large data, e.g. SAS In-Memory Statistics, SAS Visual Statistics.

Predictive Modeling:

Fitting supervised models and unsupervised analyses, as well as deploying score code.

Deep Learning:

Build deep feedforward, convolutional, recurrent networks, and variants of denoising auto-encoders. The neural networks include traditional classification, image classification, and sequence-depend-ent outcomes.

EM nodes (v14) according to SEMMA:

Association, Cluster (unsupervised), DMDB, Graph Explore, Market Basket, MultiPlot, Path Analysis, SOM/Kohonen (unsupervised), StatExplore, Variable Clustering, Variable Selection.


Drop, Impute, Interactive Binning, Principal Components, Replacement, Rules Builder, Transform Variables.


AutoNeural, Decision Tree, Dmine Regression, DMNeural, Ensemble, Gradient Boosting, LARS (LASSO), MBR, Model Import, Neural Network, Partial Least Squares, Regression, Rule Induction, SVM (supervised machine learning), TwoStage.


Cutoff, Decisions, Model Comparison, Score, Segment Profile.


Incremental Response Model, Rate Making, Survival.

Data Mining: Text Analytics (Unstructured data)

SAS Viya:

Natural language processing, machine learning, and linguistic rules. Components of SAS Visual Text Analytics: Parsing, concept derivation, topic derivation, text categorization, and sentiment analysis.


Text Mining: MapReduce (PROC HADOOP), Text Mining by SPSS Modeller; Visual Analytics by IBM COGNOS (a/k/a “Many Eyes”); Analysis of unstructured texts using Word Trees, Tag Clouds, Phrase Nets, and HISTORIO.

Advanced Analytics and Advanced Statistical Modeling
Program evaluation:
Checking for real-world effectiveness (“real” causality): Heckman's two-stage modeling, interrupted time-series, regression discontinuity, and propensity score matching

Profit-Driven Business Analytics:
Optimize decision-making processes, which, in turn, maximizes profits. Analysis based on key economic considerations and impact. Using segmentation (CLVs, SOMs), association etc. Profit-driven evaluation using misclassification costs, (expected) maximum profits, or evaluation of regression models

Causal [regression, survival] Modeling (forecasting):
e.g. Linear, Multiple, Nonlinear, Nonparametric, Ridge, Robust, Binary Logistic, Ordinal, Multinomial Lo-gistic, Hedonic, Quantile, Logit, Probit, Tobit (QLIM), Poisson (Count, Rates, Zero-Inflated[ZIP]; GENMOD); Categorical incl. Elastic Net, WLS, 2LS, Partial-Least Squares; Generalized Linear Models; Time-to-Event / Survival analysis (K-M, Proportional Hazard etc.); Multilevel Analysis/Regression etc. Also [Nonlinear] Mixed Models (MIXED, GLIMMIX, and NLMIXED procedures) etc. Time Series Modeling: Analysis of and forecasting with univariate time series: exponential smoothing, ARIMA with exogenous var-iables (ARIMAX), and unobserved components models (UCM).

Clustering and Segmentation:
Basic: Conditional approaches, random-based approaches, RFM analysis, Binning. Mathematical: Cluster Analysis (Hierarchical, k-means, Two-Step), Conjoint Analysis, Correspondence Analysis, Multi-Dimen-sional Scaling (MDS/MDA). Data Mining: Artificial Neural Networks, Multi-layer Perceptron, k-nearest neighbours (KNN), Discriminant Analysis, SOM/Kohonen, Binning etc.

Optimization Concepts:
Linear (Lagrange), nonlinear (unconstrained and bound constrained NLPs), and (mixed) integer linear opti-mization (MILP, ILP) concepts using PROC OPTMODEL.

Credit Risk Modeling:
Develop credit risk models LGD, EAD and PD (component model) according to the Basel guidelines.

Discrete Choice Modeling:
Designing a discrete choice experiment using SAS Software. Analyze discrete choice data considering num-ber of choice sets, the number of alternatives, and number of subjects.

Conjoint Analysis:
Evaluating Consumer Preferences for products and services, also performing a market share simulation with products currently in the marketplace.

Structural Equation Modeling (SEM):
Using the CALIS procedure in SAS/STAT and the PATHDIAGRAM statement in CALIS.

Latent Modeling:

  • Classes: e.g. Latent Class Analysis (PROC LCA, stand-alone SAS procedure).
  • Factors: e.g. Factor Analysis (PFA, ML, Alpha, Image, ULS, GLS, Wong’s etc.).
  • Paths: e.g. Path Analysis, Structural Equation Modeling (SEM).

Graphical Techniques: Multidimensional e.g. Preference Plots and Maps, Correspondence Plots, Scaling Plots, Individual Coefficient Plots. Design of Experiments for Direct Marketing: e.g. Experimentation, Power and Sample Size; Fractional Factorials and Orthogonal Arrays, Optimal Designs, Augmenting Designs, In-cremental Response Modeling Testing: A/B, OFAT, Split, Multi-Factorial etc.

Comparison of Effects (analysis of designs and experiments):
Parametric approaches/procedures: ANOVA, GLM, MIXED, LATTICE, NESTED, ORTHOREG, TRANSREG, MULTTEST; Multi-Level Modeling; TOST etc. Data Mining Approach: Automatic Linear Modeling. Nonparametric Approaches: Tests for location and scale differences: Wilcoxon-Mann-Whitney, Median, Van der Waerden (normal), Savage, Siegel-Tukey, Ansari-Bradley, Klotz, Mood, Conover.

Special Techniques:

  • Iterative Proportional Fitting (Small Area Estimation). See Zensus 2011 as an excellent example for raking.
  • Data/Business Process Reengineering, e.g. for Ergo (2017-2015) and Allianz (2014).
  • Six Sigma (DMAIC, FMEA, VOC/VOP), e.g. Schott (2019), Wincor Nixdorf (2011), GfK (2010-09).
  • Statistical matching of data tables: Random-based matching incl. fuzzy factor, interval-based parallelization or propensity scores.
  • Honest assessment of models and scoring of data sets.

Basics in spoken word
Very good spoken and written
Basics in spoken and written word
Mother language
Good“ (certificate)

Managementerfahrung in Unternehmen
People Management

Windows 10

Berechnung / Simulation / Versuch / Validierung
EG7.1 (SAS9.3)


Publications [more details on request]



Doctorate at Martin-Luther University Halle-Wittenberg (while working full-time).
Thesis "Cluster Analysis using SPSS”, rated "magna cum laude” (1,0).

1989 –1998

Diploma in Psychology at the University of Heidelberg.

Study focus on research Methods and applied statistics).

Starting-up of Method Consult while studying.

Certifications and Trainings

SAS Global Certifications

2016 - 2016

  • SAS Base Programming for SAS 9
  • SAS Advanced Programming for SAS 9

2017 - 2017

  • SAS Statistical Business Analyst Using SAS 9: Regression and Modeling

SAS Academy for Data Science Badges

2020 - 2020

  • Advanced Predictive Modeling
  • Predictive Modeling
  • Text Analytics, Time Series, Experimentation, and Optimization

2018 - 2018

  • Big Data Preparation, Statistics and Visual Exploration
  • Big Data Programming and Loading

SAS Data Science Badges

  • Advanced Analytics in a Big Data World.
  • Applied Analytics Using SAS Enterprise Miner (15.1).
  • Data Mining Techniques: Predictive Analytics on Big Data.
  • Deep Learning Using SAS Software.
  • Experimentation in Data Science.
  • Forecasting Using Model Studio in SAS Viya.
  • Optimization Concepts for Data Science and Artificial Intelligence.
  • Predictive Modeling Using Logistic Regression.
  • SAS Visual Text Analytics in SAS Viya.
  • Social Network Analysis for Business Applications.
  • Text Analytics Using SAS Text Miner.

SAS Advanced Analytics Badges

  • Accessing SAS from Microsoft Office Applications.
  • Advanced Statistical Modeling Using the NLMIXED Procedure.
  • Categorical Data Analysis Using Logistic Regression (14.2).
  • Conjoint Analysis: Evaluating Consumer Preferences Using SAS Software.
  • Creating BI Dashboards Using SAS.
  • Creating Information Maps Using SAS.
  • Designing, Tuning, and Maintaining SAS OLAP Cubes.
  • Determining Power and Sample Size Using SAS/STAT Software.
  • Discrete Choice Modeling Using SAS Software.
  • Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models.
  • Fitting Poisson Regression Models Using the GENMOD Procedure.
  • Fitting Tobit and Other Limited Dependent Variable Models.
  • Forecasting and Optimization.
  • Introduction to Data Curation for SAS Data Scientists.
  • Introduction to Statistical Concepts.
  • Machine Learning Using SAS Viya.
  • Mixed Models Analyses Using SAS.
  • Natural Language and Computer Vision.
  • Neural Network Modeling.
  • Personalizing the SAS Information Delivery Portal.
  • Profit-Driven Business Analytics.
  • Robust Regression Techniques in SAS/STAT.
  • Stationarity Testing and Other Time Series Topics.
  • Structural Equation Modeling Using SAS.
  • Time Series Modeling Essentials.

SAS Business Intelligence Badges incl. Enterprise Guide and Viya

  • Creating Reports and Graphs with SAS Enterprise Guide (EG 7.1).
  • SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression (EG 6.1 and 7.1).
  • SAS Enterprise Guide 1: Querying and Reporting (EG7.1).
  • SAS Enterprise Guide 2: Advanced Tasks and Querying (EG 7.1).
  • SAS Enterprise Guide for Experienced SAS Programmers (EG 7.1).
  • SAS Visual Analytics 1 for SAS Viya: Basics.
  • SAS Visual Analytics 2 for SAS Viya: Advanced.
  • SAS Visual Analytics for SAS 9: Getting Started.
  • Using SAS OLAP Cubes to Create Multidimensional Reports.
  • Using SAS Web Report Studio

more SAS Programming Badges

  • High-Performance Data Manipulation with SAS DS2 and Hadoop.
  • Introduction to SAS/ACCESS Interface to Teradata.
  • Introduction to SAS and Hadoop (9.4).
  • Programming for SAS Viya (CASL).
  • SAS Programming on the Grid (9.4 M3).

special SAS Badges

  • Fraud and Security: Fraud Detection Using Descriptive, Predictive, and Social Network Analytics.
    SAS Fraud Management: Using SAS Rules Studio.
  • Risk: Credit Risk Modeling.
  • Python/R: Using SAS Viya APIs with Python and R – VLE

other SAS Trainings

  • Advanced Credit Risk Modeling for Basel/IFRS 9 using R/Python/SAS
  • Basic Credit Risk Modeling for Basel/IFRS 9 using R/Python/SAS
  • Einführung in SAS auf dem Großrechner
  • SAS Viya 3.4 Visualization and Programming Workshop
  • SAS Visual Analytics: Introduction by SAS Institute at ERGO
  • SAS-Makro-Programmierung: Eine Einführung
  • Writing a Custom Task for SAS Studio

Other Trainings and Tutorials

  • Fraud Analytics
  • PISA Workshop: Computations with weights
  • Methods of Market/Media Research
  • Roche® Role “Statistician” at Roche Diagnostics, Penzberg DE