Data Scientist

GURKIRAN KAUR BEHL

Phone: +1-437-***-**** **************@*****.*** linkedin.com/in/gurkiran-kaur-behl-549938b

A Data Scientist with 9+ years of experience in Machine Learning, Visualization, Pricing Optimization, Logistic Regression, Data Mining, Data Analysis, Data Manipulation and Data Processing

Experience in producing innovative solutions, managing client expectations, timelines, delivering effective solutions and ability to build strong relationships

Ability to shape, sell and deliver large-scale analytics and AI programs which deliver multi-million-dollar improvement for clients

SKILLS

BUSINESS: Retail/CPG, Telecom, Pharma, Financial, IT & Services

TECHNOLOGY: R, SAS (EG & Base), SQL, Tableau, Power BI, Python, Excel, VBA

MATHEMATICS: Statistics, Regression (Linear, Logistics), Pricing Optimization, Clustering (K-Means, KNN), Time Series, Market Mix Modelling

AWARDS

STAR PERFORMER AWARD – ACCENTURE

Catalyst award for delivering outstanding performance in converting POCs and stabilizing project. Automating the modelling process and handling ad hoc requests, team and meeting deadlines and going beyond client expectations

GOLD AWARD – GENPACT

Appreciation awards for building logistic regression framework to analyse customer lifetime value

EXPERIENCE

FUNCTIONAL & DATA ANALYTICS CONSULTANT, ACCENTURE JAN 2013 - AUG2019

Pricing Optimization - UCM and Mix Modelling

Created a framework of revenue optimization for a US Retail Giant to mark up the labour and material cost, based on Pricing and promotional elasticities which resulted in revenue growth of $30M

Estimated the impact of promotions/discounts on sales of 3000 SKUs using UCM technique. Developed an automated parallel processing framework in R to tackle huge amount of data (2 billion rows). This helps us measure the price elasticity and promo elasticity for each product.

Developed and Deployed simulation model using Non- Linear GRG to judge the right price and discount combo based on business demands. This feeds into the pricing decisions of the firm in order to optimize revenue.

Marketing Spends Optimization - Market Mix Modelling

Enhanced revenue for an US IT major by optimizing the marketing spends and creating an interactive dashboard to depict several Spends and Revenue combo. Provided actionable insights to Non-labour costs portfolio team by predicting upcoming costs using regression concepts in R, Python, Tableau.

Developed predictive models using Supervised learning (multivariate linear regression) in R to estimate the marginal effect of promotional activities on sales of commodities. Market mix Modelling technique was used to decompose the total sales into base and marketing driven sales.

Recommended an optimization model to reallocate investments towards marketing drivers having higher marginal ROI. Estimates for all the marketing drivers were used to create Response curves and calculate the marginal ROI.

Analysis of Long-Term TV vs Digital Media Impact

Delivered strategic business insights to client on budget allocation of marketing tactics. Merged data using SAS and R to study marketing trends in numerous domains: Retail, pharma, CPG, Auto & Finance.

Quantified the long-term impact of traditional media over digital media via ROI and marginal ROI. A white paper was published on this study. Created an advanced visualized dashboard using Tableau and Power BI to analyse market trends and provided insights on budget allocation.

Budget Allocation of Media Drivers

Facilitated a leading retail customer to diversify marketing media budget using Python and R tools to build sustainable competitive strategy. Python based Web-Scrapping was done to fetch competitive pricing information. That helped to gain 25% increase in footfall.

Provided insights to stakeholders using SQL queries, R- predictive analytics model on spends behaviour across industries.

SENIOR DATA ANALYST, WIPRO DEC 2010 - JAN 2013

Provided advanced analytics solutions to BFSI clients using tools like SAS in predicting churn rate, cross-sell/up-sell models

Extracted, reviewed and prepared sales and customer behaviour data for clustering, reporting using SQL. Decision tree algorithms in SAS are used to create customer profiling and predict the profitable cluster of customers

Developed SAS advanced analytics POCs to showcase Analytics team’s capabilities to new strategic wins

Identified and executed initiatives with Product, Sales team to improve process and data infrastructure

Understood the sensitivity of business and Strictly adhere to legal and compliance guidelines regarding access and exposure to sensitive and confidential information

Identified business problems, created RACI matrix, SOW, proposals and list of activities to deliver as an output for finance organization

Training & Mentoring new joiners around data analytics with SQL, SAS

DATA ANALYST, GENPACT JUL 2008 - DEC 2010

Collection Scorecard - Auto Insurance

•Built and implemented collection scorecard for leading auto insurance provider using GAM theory which predicts delinquency behaviour of a customer

Created SAS programs to collect, removed data outliers, laid model assumptions

Performed exploratory analysis on variables, variable reduction technique used in SAS to finalize the metrics. Model was built to predict 30+ or 60+ days delinquent behaviour

Customer Lifetime Value Model and Volume Forecasting - Credit Cards

•Prepared ad-hoc reports and dashboards using UNIX, SAS, Teradata and SQL to analyse the changing behaviour of customers, delinquency rates and their transaction patterns for financial industry

Designed the analytical framework for sales forecasting of different market segments in the credit cards industry, that ties together customer demographics, cross-shop information, product action, overall-industry forecasts and segment specific macro-economic indicators into a single Vector Autoregressive (VAR) model.

Efficiently delivered monthly reports on sales, campaign effectiveness, identified trends and automated the process

Deployed models using OLS and logistic regression which predicts the customer credibility at the time of acquisition and that helped client to retain its customers and overall 2% decrease in delinquent customers

EDUCATION

MASTERS IN STATISTICS, PUNJABI UNIVERSITY, PATIALA, INDIA 2008

Gold medallist with 90% marks

Graduated with scholarship

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