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Leonardo E. Auslender

Leonardo.Auslender @ gmail . com

732-***-****

Edison, NJ

https://www.linkedin.com/in/leonardoauslender/

PROFILE

Senior Statistician/Economist/Data Scientist with extensive experience in analytical data modeling, data mining and forecasting for industries including petroleum, cosmetics, pharmaceuticals, banking, biology, communications, direct mail marketing, insurance, retailing, web-marketing and software. Thoroughly experienced in all phases of statistical and econometric analysis, computing, system implementation, and reporting. Skilled in working with large and complex datasets in the areas of statistics, data mining and artificial intelligence. Noted for ability to work independently, as a team player or manager, and for rapidly analyzing and resolving problem situations. Strong communication skills. Delivered numerous presentations and papers. Native speaker of Spanish and fluent in other languages.

QUANTITATIVE SKILLS

Statistical, Machine Learning and Data Mining analysis of predictive models, segmentation, marketing applications, direct mail marketing, customer life time value, specialized in advanced methods, credit cards and mortgage analytics.

SOFTWARE

SAS/BASE, SAS/MACRO, SAS/STAT, SAS/ETS, SAS/GRAPH, SAS ENTERPRISE-MINER, SAS-SQL, SAS/IML, CART, UNIX, SAS_SQL, Redshift SQL, Python. PROFESSIONAL EXPERIENCE

PRINCIPAL ANALYTICS PREP 2017 October to Present

Faculty Member (Part-Time)

• Teach statistics, Data Mining and SAS programming with industrial applications on propensity to purchase, demand and classification analysis, finance and insurance. Accelerated class for data scientists covering a wide spectrum of statistical and advanced data mining and Bayesian techniques, such as Trees, Random Forests, Gradient Boosting and Penalized Likelihood Data Augmentation.

BED BATH AND BEYOND 2017 August to 2017 October.

Sr. Data Scientist.

• Recommended, designed and programmed method for estimating time to delivery of e-commerce orders with 80% of probability. Designed structure of neighborhood of zip codes to facilitate search and operations for delivery.

QUINTILES / IMS HEALTH 2017 June to 2017 August

Data Scientist

• Analyzed and created recommender systems for salesmen visits to doctors. Recommendation System included Adverse Effects, alternative doctor visits and frequency of visits. Provided full methodology for adverse effects detection and fraud. 2

CISCO SYSTEMS 2017 May

Statistician.

• Analyzed data distributions by way of Finite Mixture and Cox-Models Models. Created SAS system for that purpose.

NOVO NORDISK 2017 March to 2017 April

HEOR Statistician

• Analyzed Truven databases and profiled Diabetes 2 patients yearly counts, drugs usage and cost segmentation. Produced reports on patient evolution of diabetes. DEVEXI 2016 May to 2017 February

Data Scientist.

• Validated epidemiological studies, both case-control and cohort studies by advanced statistical methods to identify confounder effects. Imputed missing and wrong date of birth in extensive claims tables. Contributed to design of graphic information of study diagnostics. In charge of statistical help notes, verification of statistical code used in proprietary tool and overall recommendation of statistical/epidemiological methodology.

CISCO SYSTEMS INC. 2014 Dec. to 2016 March.

Senior Statistical Research Consultant (through BIS Consulting).

• Analyzed transition of sales funnel via Hidden Markov models and Unobserved components models

(UCM).

• Created system for forecasting bookings (total budgets) of technology product demand and customer life time value via an ensemble of time series methods for worldwide customer base with corresponding reports. Successfully implemented to orient sales efforts worldwide.

• Created system for customer base English-like profiles for easy access by sales force with special emphasis on KPI.

• Models developed in SAS, SAS_SQL, R and Matlab.

• Cross-sell and upsell models of technology propensity to purchase with special focus on identification of outlier subgroups. Successfully implemented and in use to orient sales force.

• Presented all projects descriptions and conclusions to management and colleagues with recommendations based on the research.

SANOFI PHARMACEUTICALS 2014 Nov. to 2014 Dec.

Senior SAS Programmer (through Rangam).

• Created SAS system to create database of time dependent data of pharmaceutical marketing data that automatically updates on a weekly basis while ensuring correct alignment of time dependent fields.

NEW YORK STATE INSURANCE FUND 2014 March to 2014 October Senior Statistical Research Consultant (through IBI Group).

• Analyzed workers compensation claims for prediction of medical costs reserves via longitudinal data analysis, time series and Cox survival models, and the creation of empirical distribution function of 3

medical claim costs. Project difficulty lay in the uncertainty about number of claims per claimant to estimate reserves. Predictions allowed to lower initial estimated reserves with considerable savings.

• Created all requested models for claims reserves and permanency, documented and reported findings of Exploratory Data Analysis, Profiling and Models fully in Tables and Graphics with SAS tools. Created structure for model of fraud identification of claims based on provider, claims, frequency and injury type.

• Designed and created data warehouse structure for analysis with 10 million records by way of SAS_SQL and SAS data tools.

CISCO SYSTEMS INC. 2012 March to 2013 December.

Senior Statistical Research Consultant (through BIS Consulting).

• Created clustering and inferential tools and reporting for Marketing Data bases to be used by sales force.

• Analyzed Web Usage in enhancing customer response, retention and prospecting models. Consulted on text mining models.

• Created data mining system in SAS that automatically imputes missing values, creates comparative models, assesses and selects optimal ones and score them, while fully reporting every single step for cross-sell, up-sell and attrition analysis with emphasis on computational speed and reporting. TD BANK. 2011 August to 2012 March.

Lead Statistician – Fair Lending.

• Created modeling system to comply with Dodd-Frank legislation to validate possible discriminatory cases based on comparison of results between Naïve-Bayes (which included creating two variable selection methods) and logistic regression. Alert system was based on a comparison of ROC curves with the caveat that its application is the opposite of the one used in Data Mining. Successfully verified and submitted reports to regulatory agencies.

• Derived full reporting and alert method for managers for fair lending issues.

• Initiated modeling of possible discriminating cases for data up to late 2011. SAS INSTITUTE, INC. CARY, NC. 1998 April to 2011 July. Research Statistician, in Enterprise Miner Department: 2001 April to 2011 July. Main statistician for creation of newer data mining tools and research on methods and prospective applications. Promoted from consultant position below.

• Implemented Market basket solution in the SAS Banking Solution for use in business applications.

• Created Naïve Bayes system, new methods for variable selection (GSForward), Cutoff techniques, Net Lift and propensity Scoring and model comparison techniques for the Enterprise Miner.

• Gave lectures on applied and theoretical variable selection methods, market affinity analysis, visualizing correlations and redundancy. Wrote documents on implementation of Naïve Bayes, Bayesian Networks and MARS.

• Published paper on market affinity analysis that leads to recommender systems.

• Created co-linearity vaccine that allows for variable selection while controlling for co-linearity.

• Worked in collaboration with Target and HSBC implementing net-lift solutions. Statistical Consultant, Analytical Consulting Department: 1998 April to 2001 April.

• Performed statistical and data mining consulting and research for business applications and taught courses on software and statistical methods.

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• Created automatic signal detection methodology in the pharmaceutical industry for adverse effects of drugs (from empirical Bayes methods by W. Dumouchel) for major pharmaceutical industry.

• Analyzed home insurance claims for P&C to understand insured moral responsibility/negligence in claims for major insurance company. Started methodology to predict associated payouts.

• Performed SKU price elasticity and stocked-out analyses (missing sales opportunities) for Wal-Mart. Implemented hybrid method of market basket analysis and revenue optimization.

• Analyzed Point of Sale Data for Blockbuster by creating a recommender system. Previous Positions.

CHASE MANHATTAN BANK CORPORATION, EDISON, NJ

Vice president, Consumer profitability Department: Managed and performed statistical analysis of mortgage default and prepayment, market segmentation, scoring and NPV estimation, data base marketing and data warehouse design and creation.

AT&T / BELL LABS, BASKING RIDGE, NJ

Direct Mail Modeling Manager / Statistical Researcher: Bell Labs Statistics Research Department: Led market research of new business development and direct mail modeling campaigns. RUTGERS UNIVERSITY, NEW BRUNSWICK, NJ

Adjunct Professor, Graduate Department of Management. Taught Finance and Macroeconomics. STATISTICAL AND SAS CONSULTANT.

For Schering Plough and Salomon Brothers, and other assignments. CITIBANK, INC, NEW YORK, NY

Senior Business Analyst, Credit Card Analysis: responsible for analysis of response and solicitation and of campaign performance.

AT&T, BEDMINSTER, NJ.

Statistician and economist, Market Analysis and Forecasting: Responsible for statistical analysis of phone usage and demand elasticity.

EDUCATION

Abd, University of Illinois, Economics (completed thesis on Structural Inflation) M.S., University of Illinois, Economics

M.S., University of Illinois, Statistics

M.A., University of Connecticut, Economics

Licenciado (M. A. equiv.), Univ. de Buenos Aires, Economics NATIONAL STATUS - US Citizen.

SOME RECENT PUBLICATIONS AND LECTURES.

2019/05, Lecture on Visual Tools for Explaining Machine Learning Models, presented as Principal Analytics Prep Events, NYC, NYC, available at https://www.slideshare.net/LeonardoAuslender/visual-tools-for-interpretation-of- machine-learning-models.

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2017/03, Webinar, “Gradient Boosting and Comparative Performance in Business Applications”, DMA, https://thedma.org/webinars/analytics-community/gradient-boosting- comparative-performance-business-applications/

2015/09, lecture on “The issue of classification versus precision rates in comparing classification models”, at the NYC Informs Society, available at http://nymetro.chapter.informs.org/prac_cor_pubs/09- 2015%20LeonardoAuslenderClassifPrecisionLift.pptx.pdf 2012/04, submitted paper to Statistical Science, “Finding Important Genes from High- Dimensional Data: An Appraisal of Statistical Tests and Machine-Learning Approaches”, with C. Wang, J. Gevertz, and C. Chen.

2011/03, lecture on “Variable Selection in the Linear Regression Model”, The College of New Jersey, Ewing, NJ.

Most papers available at https://independent.academia.edu/Auslender, or https://www.slideshare.net/LeonardoAuslender/presentations or by request.

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