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Chief Data Scientist

offline
  • auf Anfrage
  • 97333 Corvallis
  • Weltweit
  • de  |  en
  • 18.05.2022

Kurzvorstellung

Over the past 20 years I have developed the ability to speak business and apply predictive analytics. I have managed the development of statistical software to solve business problems in the financial, retail and high-tech industries to name a few.

Qualifikationen

  • Apache Hadoop
  • Apache Spark
  • C++
  • IBM SPSS Statistics
  • Microsoft SQL-Server (MS SQL)
  • MongoDB
  • Python
  • SAS Business Intelligence (BI)
  • SAS (Software)
  • Scikit-learn

Projekt‐ & Berufserfahrung

Data Scientist
HIMSS (Healthcare Information and Management Syste, Chicago
6/2016 – 7/2019 (3 Jahre, 2 Monate)
Gesundheitswesen
Tätigkeitszeitraum

6/2016 – 7/2019

Tätigkeitsbeschreibung

Supporting HIMSS in providing sale and marketing predictions for their end-clients. This is an OEM (or white label) implementation of Modern Analytics software (Model Factory) to provide a self-service predictive marketing kiosk, automatically merging end-client’s SFDC data with HIMSS data and then building and scoring a lead prioritization algorithm.

Eingesetzte Qualifikationen

SAS (Software)

Data Scientist
Reliant Funding, San Diego/New York
2/2016 – 1/2019 (3 Jahre)
Banken
Tätigkeitszeitraum

2/2016 – 1/2019

Tätigkeitsbeschreibung

I created the analytic roadmap for a full Basel risk implementation to decrease write-offs across a variety of portfolios. On a single portfolio, increase in revenues where around $15million on a portfolio of $88million – a near 20% increase, eliminating about half of the bad loans that were underwritten before. I supervised the data implementation of various systems (MS SQL, Salesforce.com and live credit feeds), the creation of the models (P2D, EAD, LOG) for application and behavioral scoring (15 models in total). When creating the capital at risk summary, about $5million of the reserve pool was freed up to support further underwriting of more loans.

Eingesetzte Qualifikationen

Basel II / Basel III, Risikomanagement (Finan.), Microsoft SQL-Server (MS SQL), MongoDB, SAS (Software), Python

Data Scientist
Schlumberger, Houston, TX
11/2015 – 12/2018 (3 Jahre, 2 Monate)
Öl- und Gasindustrie
Tätigkeitszeitraum

11/2015 – 12/2018

Tätigkeitsbeschreibung

I worked for the unconventional side of oil services to supplement business processes with the use of statistics/machine learning. Projects include predicting cement setting times for slurries, 6-month to 5-year flow predictions, predicting contribution margin on project bids throughout the bidding process and identifying lost charges during project implementation.

Eingesetzte Qualifikationen

SAS (Software), Scikit-learn, Python

Data Scientist
Thermo-Fisher-Scientific, Carlsbad, CA
1/2014 – 6/2018 (4 Jahre, 6 Monate)
Life Sciences
Tätigkeitszeitraum

1/2014 – 6/2018

Tätigkeitsbeschreibung

Supported their marketing efforts through the maintenance of 1,000+ equations globally. The data and models get refreshed on a monthly basis and include propensity-to-buy models, retention models, life-time value and satisfaction scores.

Eingesetzte Qualifikationen

Statistik (allg.)

Data Scientist
IBM, Armonk, NY
1/2005 – 1/2015 (10 Jahre, 1 Monat)
IT & Entwicklung
Tätigkeitszeitraum

1/2005 – 1/2015

Tätigkeitsbeschreibung

I supported IBM’s tactical, as well as strategic marketing in multiple ways. Key tasks include concept development and implementation of traditional B2B marketing through the use of predictive analytics. Other tasks included the creation of a dynamic CRM modeling framework that allows for real time scoring of IBM’s sales pipeline in order to predict time-to-close and likelihood-to-win for every sales opportunity world wide. Besides having very actionable predictions that have helped IBM meet its quarterly goals in all of its divisions (software, hardware, services), the dynamic CRM modeling environment also helped in the identification of emerging trends through the study of the changes in the model parameters and other key factors. We also developed the concept of eNurture, a way of nurturing leads through the sales cycle by predicting next marketing touches.

Eingesetzte Qualifikationen

IBM Cognos, IBM SPSS Statistics, SAS (Software), Lotus Notes Script

Data Scientist
Freeman and Associates, Rancho Santa Fe
1/2005 – 6/2007 (2 Jahre, 6 Monate)
Banken
Tätigkeitszeitraum

1/2005 – 6/2007

Tätigkeitsbeschreibung

Migrated a 4.5billion+ hedge fund from multiple systems, data bases, and languages into a single SAS environment. The system, developed within budget, allows for rapid sampling of all major data providers (IBES, CompuStat, Barra, etc) to create the required data sets for portfolio optimizations and evaluations. The system creates time-series, as well as cross-sectional data sets in seconds, compared to weeks using the old system. The data sets are split-adjusted, takeover cleaned, and have all ticker changes handled. To create the system, 100’s of scripts, ranging from pearl to oracle had to be translated (in some cases reverse engineered due to loss of source code). In addition, a common key was invented to make the merging of data sets transparent.

Eingesetzte Qualifikationen

SAS (Software)

Data Scientist
VCA ntech, Los Angeles
10/2002 – 12/2005 (3 Jahre, 3 Monate)
Gesundheitswesen
Tätigkeitszeitraum

10/2002 – 12/2005

Tätigkeitsbeschreibung

VCA provides veterinary services in a retail environment. I designed, sourced, and built a CRM system which utilizes 4-years of transactional data from Oracle warehouses and integrated SAS statistical/data mining solutions. Currently approx. 40 product specific statistical models are built and scored on a monthly basis to increase product penetration for various animal products and treatments. One product model is built for 1st-time and repeat buyers, separately. The system increases revenues by $25million on a $450million revenue baseline. The CRM system includes a budgeting mechanism based on potential revenue gains – making all channel marketing dollars focused and accountable. In addition to the internal up-selling and cross-selling through various channels, client acquisition models were built, and a routine monthly mailing established. This includes backend analysis and response feedback loop to refine modeling efforts. Currently the acquisition engine adds 50,000 new clients a year (or revenues of $20mill/year – first year revenues only).

Eingesetzte Qualifikationen

Oracle Database, SAS (Software)

Data Scientist
UBS, Basel/Zuerich
6/1996 – 12/2002 (6 Jahre, 7 Monate)
Banken
Tätigkeitszeitraum

6/1996 – 12/2002

Tätigkeitsbeschreibung

Documented the analytical process used for risk and later for marketing. This effort resulted in a 400-page document that outlines step-by-step instructions of the analytical process used in data mining. With the completion of the documentation, I was contracted to program the automation of the analytical process as much as possible. The SAS, SAS Enterprise Miner, and NeuralWare based, automated modeling environment reduced the model build time from 2 weeks to 2 days. This, and previous work at UBS/SBC, received the European SAS enterprise computing award for 2000. The addition of direct marketing to the retail client base at UBS turned the money-losing retail sector into a profitable business for UBS. The work has been the analytical backbone for UBS to date.
I was also contracted to plan, design, and manage the data feeds from multiple legacy systems to build the banks central analytical data mart, which supports risk and marketing at SBC (now UBS) for its retail and private banking units. The system is an analytical view of a 3-year transactional data history. The analytical work performed for UBS included risk and marketing models using advanced statistics, neural networks, and genetic algorithms. Additional consultation included the analytical department design, tools creation, tools evaluation, hardware sizing to support analytics, and other marketing issues such as how to analyze Internet data, performing ‘flows of funds’ analysis, and product portfolio optimizations. At UBS, I managed the entire analytical process from raw data to backend analysis to ensure that marketing efforts were successful. This work won the SAS Application of the year in 2000, and also became the foundation of SAS multiple SAS solutions now offered world wide

Eingesetzte Qualifikationen

SAS (Software), C++

Data Scientist
American Express, New York, NY
1/1994 – 2/1996 (2 Jahre, 2 Monate)
Banken
Tätigkeitszeitraum

1/1994 – 2/1996

Tätigkeitsbeschreibung

Built statistical and neural network models for American Express Cards Division and Customer Services using SAS. The analytical work included cluster and CHAID analysis, binary response, two-stage, and attrition models to support the personal (green, gold, platinum) and corporate cards. Other responsibilities included written documentation, and client consultation on analytical techniques, sampling issues, and analytical strategy.

Eingesetzte Qualifikationen

SAS (Software), Deeplearning4j

Data Scientist
RCI, Indianapolis,IN
8/1993 – 6/1995 (1 Jahr, 11 Monate)
Wohnungswirtschaft
Tätigkeitszeitraum

8/1993 – 6/1995

Tätigkeitsbeschreibung

Predicted call volumes for a 500+ people call center 6 months in advance to allow for proper staffing. The work utilized time series methods (ARIMA, Spectral) to predict daily call volumes and call distributions - 95% of the forecasts where within 3% of the actual values. Other work included applied queuing theory to prioritize workloads throughout the day, and predicting supply, demand, and utilization for inventory management using ARIMA. The improvements to the forecasts using ARIMA resulted in a 5% increase in inventory utilization (a $10million improvement).

Eingesetzte Qualifikationen

IBM SPSS Statistics

Ausbildung

Mathematik
Butler Univeristy
1992
Indianapolis

Über mich

I have both, German and American citizenship.

As an innovator, I have received US patents for building what is now considered deep learning neural networks based on genetic algorithms to solve applied statistical problems. This includes the derivation of final attributes from raw data. This works has since led me to coining the term “autonomous analytics”.

I provide the “big-picture” vision, but also have the required project management skills, as wells as hands-on experience, to deliver on complex analytical/big data projects. My hands-on experience includes applied statistic, machine learning, data aggregation/manipulation, and process automation. My management experience ranges from managing small statistical departments to leading a 20-person developer team.

Statistical methods which I have applied to real world business problems and shown to provide Return-On-Investment include various forms of regressions, neural networks, fuzzy logic, genetic algorithms, tree-based methods, Bayesian classifiers and many flavors of time series analysis.

In terms of Big Data, I have built analytical solutions against complex data base structures with as many as 120+ global data sources as well as Hadoop-like data bases that included 55Billion rows and 50,000 independent data attributes (about 700TB).

Companies I have consulted for over the past 25 years include:
High-Tech: EMC, IBM, Juniper Networks, Nutanix
Automotive: Carfax, KBB, Mitchell 1, US Autoparts, Volvo, Daimler
CPG: Symphony IRI, Marketshare Partners
Entertainment: Paramount Pictures (VIACOM)
Financial: Bank of America (treasury), UBS (risk and marketing), American Express, Clarivest Asset Management, Freeman and Associates, Goal Structured Securities, Wescom Credit Union, MCA
Hospitality/Gaming: Harrah¹s Rincon, MGM
HR: ADP
Insurance: State Farm, Travellers
Internet: Big Fish Games, Classmates.com, Expedia, Valueclick Media
Manufacturing: Siemens, Sikorsky, Thyssen Steel, Schlumberger
Non-Profits: American Red Cross, Gates Foundation
Retail: Garden Fresh Restaurant, Limited, Macy’s, Musician’s Friend, TaylorMade, VCA Antech (now Mars)

Weitere Kenntnisse

Software Solutions Implemented:
SAS Risk Dimensions, SAS Credit Risk Scoring Node, SAS Optimization, SAS Marketing Automation, SAS BI, SAS High Performance Forecast Server

Various Campaign Management Tools

Various Business Intelligence.

Productivity Tools:
Microsoft Office, including MS Project and Visio, code versioning and bug-tracking tools, and a variety of client demanded tools.

Programming:
SAS, C, VB, Python, R.

Persönliche Daten

Sprache
  • Englisch (Fließend)
  • Deutsch (Muttersprache)
Reisebereitschaft
Weltweit
Arbeitserlaubnis
  • Europäische Union
  • Vereinigte Staaten von Amerika
Profilaufrufe
2124
Alter
54
Berufserfahrung
31 Jahre und 2 Monate (seit 01/1993)
Projektleitung
20 Jahre

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