Data Science Analyst/Marketing Analytics Resume NJ - Hire IT People

SUMMARY:

Analytics professional with public and private sector domain experiences, domestic and international; Experienced in applying econometric, machine learning and probabilistic modeling techniques to strategic or operational challenges.

TECHNICAL SKILLS:

Programming Skills: SQL, R, Base SAS and Python (Pandas and SK Learn)

Technical Analysis & Statistical Tools: Google Analytics & AdWords, Adobe Analytics, A/B Testing, Qualtrics

Business Intelligence & Data Visualization and Warehousing Tools:Microsoft Power BI, Tableau, Qlik Sense

Statistical & Machine Learning Techniques: Text Mining, Natural Language Processing, Web - crawling, Web-scraping

PROFESSIONAL EXPERIENCE:

Confidential . (NJ)

Data Science Analyst/Marketing Analytics

Responsibilities:

Currently handling multiple projects, responsible for analytics delivery ranging from understanding business problems, Data Needs Identification, architecting, validating, presenting insights.
Extensively involved in Project Management, Service Delivery and analytics solution design activities meeting diverse analytical needs for varied clients across the globe.
Modeling Uplift to predict a key prospect's likelihood to a campaigns thereby improving optimal allocation of marketing spend and improvement in ROI by 20%.
Developed Statistical Models for identification of key customer segments for upselling, cross selling using segmentation techniques to drive volumes and made product recommendations using transactional and behavioral data.
Churn Modeling for identification of moment of churn, key churn drivers thereby improving customer retention by 30%.
Market Mix Modeling, Attribute drivers modeling, Price Promo Modeling, for US CPG industry for introduction of new products, increase in sales of existing products with promos, to measure competitive advantage in pricing and brand attributes.
Generated assortment optimization insights for optimal placement of SKUs in stores across European markets for a CPG major thereby reducing non performing SKUs and opportunities to increase topline by 10%.
Executed pre - sales activities for multiple clients by creating response to new proposals, Solution Designing, Business Case formulation, Proof of Concept (POC) and Value (POV), estimation etc.

Confidential (Westport CT)

Data science Analyst/Business Analyst

Responsibilities:

Developed a robust and reusable data validation framework in SAS for addressing data quality issues and reducing turnaround time of analytics projects.
Developed automated SAS process for forecasting market size of key categories for a CPG major thereby enabling faster decision making for CBD teams.
Execute regression models, understand output, perform hypothesis testing, and develop insights that relate back to business.
Developed multiple Business Intelligence dashboards/score cards using a variety of platforms as Spotfire, Excel and mobile reporting to answer key business persona questions pertaining to Consumer, Market, Brands, Channels, Competitors etc.
Engaging with business to in onsite, offshore discussions for understanding the analytical, dash boarding requirement and ensuring end to end execution of projects with utmost quality.
Identification of best methodologies for analytical projects ensuring quality, faster turn - around time and re-application.
Identified and achieved new business from existing clients through identification of new opportunities and effective stakeholder management.
Traveled to US and Singapore for Client meeting and POC discussions.

Confidential

Data Science Analyst

Responsibilities:

Response and Revenue modules for Telecom Client
Development of Best Response and revenue for telecom client to know whether customer would be gone for Renewal of the Annual plan and what is the Revenue expectation out of the renewed customer.
Further to Non-response customer Acquisition of the existing customers who are not renewed within not responded for any marketing activities.
Understanding the Business requirement and Study the data Dictionary
Preliminary data Preparation and validation
Variable selection from Each subject area and reduction of variables
Using the best model and Validation of the results with existing module.

Confidential

Associate Consultant

Responsibilities:

Generating the Weight on the Demographic and Non - demographic Variables to make the dataset unbiased by using the SAS techniques as like PROC FREQ, PROC REG, PROC MEANS, PROC UNIVARIATE, SAS/MACRO, SAS/SQL, etc.
Developed the SAS Code for different products by using the documentation provided by the onsite team.