## BIO
NEERAJ VERMA (Chief Statistician, OnMobile Global) [login to view URL]. (Stat.) Location: Bangalore (INDIA) SUMMARY Experience journey: Over 8 years of overall experience, Including 4 years as a Lecturer (Statistics) at PG level and almost 2.5 years in Quantitative analyst role in which I have had done database statistician task like data Analysis, manipulation, Statistical research, system study and Modeling. Currently I am holding a position of Chief Statistician in which I performing Consultative roles in Predictive Analytics and Data-Mining methods for Business intelligence Applications Development from almost last 2 years. Keywords Data Mining, Risk Management, Market Segmentations, Statistical Consulting, CRM, SAS STAT/ EG/ EM CRM Models: I am involved in various projects and developed various Statistical Models through extensive use of Data-mining on very large customer-base (3Mn-50Mn) on CRM during last couple of years. Some of the most successful Models implemented during various Marketing Campaigns are as follows:- • Customer Segmentation Model • Customer Growth Model • Customer Acquisition Model • Customer Loyalty Model • Customer Survival Model • Cross –Sell & Up-Sell Model • Purchase Model • Collaborative Filtering • Server Utility Optimization Model • Price Elasticity Analysis • Churn Affinity Model • Churn Propensity Model Mathematical Models: During the last couple of years I studied, developed, and fitted the some of the best Mathematical Models like- Standard (Linear-Non Linear) Model, Generalized (Linear-Non Linear) Model, Mixed (Linear-Non Linear) Model, and Generalized- Mixed (Linear-Non Linear) Model Publications • [2009] â€Logistic Regression Model: A Data-Fitting approach using SAS †(OnMobile Global) • [2008] â€Market Mixed Model: A Market Basket Analysis approach using SAS†(OnMobile Global) • [2008] “Churn Predictive Modeling: A Data-Mining & Modeling Approach†(OnMobile Global) • [2007] “Effective rates of advertisement†(Indian Express) • [2007] “Discriminant Analysis of Indian Leading English Newspapers†(Indian Express) • [2006] “A Modal Monitoring program to ensure ongoing performance and focus on redevelopment efforts†(ICFAI) • [2006] “Service Quality Measurement instrument†(ICICI Bank) Team Management I do have more than 3 years Experience in training, motivating and handling team of Statistical Analysts, SAS Programmers and Data Analysts. PROFESSIONAL EXPERIENCE Chief Statistician OnMobile Global (p) Ltd (April 2008 to Present) Role & Responsibilities: Performing a role Statistician, handling churn Management, risk Management and marketing campaign projects for National and International clients. Main day to day activities: Work with business partners in defining business goals, project requirements and create project specifications for their team. Mentoring and guiding team of SAS Programmer/ Statistical analysts in departments, and business partners in the design and execution of their own as well as their team's business projects/processes. Breakthrough innovations in the areas of Statistical Analytical Models (Supervised and Unsupervised Classification and Predictive Models) for Marketing Research, Risk Management and CRM Analytics. Performing Analysis and Evaluation of Models for measuring risk and profitability • Developing and enhancing targeting Models, executing marketing campaigns (AD-RBT, X-sell & up-sell), analyses of acquisition performance to support international markets acquisition strategy and initiatives. • Conducting extensive Data-Mining of large databases to analyze marketing programs performance, productivity by segment and generate detailed reports that drive insight and highlight opportunities for incremental revenue, churn improvement, and increased customer satisfaction. • Developing and effectively present, graphically and verbally, Analysis of data that support developed hypothesis (product and service usage Analysis, life time value estimation, pricing Analysis, service channel usage Analysis, Next product recommendation, etc.) • Provide expert analytics services to business specialists/managers this includes preparation of reports that drive insight & highlight opportunities for incremental revenue, churn improvement & increased customer satisfaction. • Provide leadership and creativity to identify opportunities to apply Statistics, Data-Mining, Modeling, and Advanced Analytics to bring value to the business • Provide input into development and implementation of performance reporting methodologies for Model Validation. • Review and analyze final results to interpret and summarize findings for senior management, and recommend and defend appropriate strategies. • Timely and accurate delivery on agreed upon requirements of project plan, communicating unexpected delays. Tools: MYSQL, EXCEL 2007, SAS/BASE, ACCESS, STAT, IML, ETS, EG, E- MINER [Environment/OS: ODBC, ORACLE, V9, Window 2003 NT SERVER] Statistical Analyst Fifth C Solution (p) Ltd (Sep 2007 to April 2008) Role & Responsibilities: • Leads the Statistical Analysis life cycle from Model development life through results interpretation and deployment. • Defined methods of Analysis, defined data requirements, and designed data collection/quality assurance methods ensuring that all relevant information is obtained and controlled according to legal and company guidelines. • Developed rules, benchmarks, etc. for the Analysis and interpretation of Model data • Provide expert Statistical direction in the design of analytical approaches, development of sampling methodologies, estimation of sample sizes, and presentation of results. • Provide expertise in data Modeling and Analysis while maintaining a focus on sampling methodologies and protocols. • Developed targeting protocols for identifying Statistical outliers • Developed and implemented Statistical Models for predicting fraudulent behavior (Predictive Modeling), Neural Networks, and Mathematical Models • Estimating sample size requirements and determining sample design and weights to support ongoing fraud investigations Modeling & Analytics • Developed Statistical Models for targeted acquisition, retention, cross-sell, customer lifetime value or behavioral segmentation Models using Predictive Modeling techniques that include linear and logistic regression, cluster Analysis, neural nets and other Statistical techniques. • Customer Segmentation (behavioral and value based), Customer Profiling, Scoring, Customer Equity and Lifetime Value Modeling • Acquisition Model – prospect targeting, prospect potential value, product positioning, key influencers, product awareness • Retention Model – loyalty, store choice, customer service, satisfaction, propensity to churn, attrition Models • Cr
## Area of Expertise
PROFESSIONAL TRAINING I had successfully completed professional training in the following areas from SAS Institute (P) Inc • SAS programming I: Essential • SAS Programming II Manipulating data with DATA Step • Query and Reporting using enterprise Guide • Enterprise Guide: ANOVA, Regression and Logistic Regression • Applying Data-Mining Techniques using Enterprise Miner Software I had successfully completed professional training in the ORACLE 9i (DBA) from CDAC Noida • SQL • Fundamental I • Fundamental II • Performance & Tuning Self study/Experience: • SAS Programming III • SAS ADVANCE Macro • SAS ADVANCE [Performance & Tuning] STATISTICAL TECHNIQUES/MODELS USED: Regression Techniques • Linear/ Non-Linear Regression Discrete Choice Models • Logit Model • Probit • Multinomial Logistic Regression Techniques • Survival Analysis Multivariate Techniques • Conjoint Analysis • Choice Based Conjoint Analysis • Discriminant Analysis • Cluster Analysis • Factor Analysis Optimization Techniques • Linear/Non-Linear Programming • Decision Tree Optimization Artificial Intelligence Techniques • Neural Networks
