Neha Shukla

Skilled Business/HR Analyst with eight years of experience in Opinion Research and HR industry. Experience in designing, implementing, and measuring various people-related functions for cost-effectiveness, continuous improvements, and impact-based prioritization. Excellent communication and presentation skills having worked with C-suite executives, large cross-functional teams and reputed external partners.

• Rigorous academic projects in analytics with peer-reviewed publications (see additional information)

• Motivated self-learner with supplementary domain knowledge and certifications e.g. HRCI® issued PHR certification for US labor laws, Google Ads Certifications for digital advertising etc.

Key Academic Projects:

● An Analysis on the Starbucks Mobile Ordering System.

Analyzed declining usage and sales of Starbucks Mobile ordering app. Designed surveys, collected data and performed statistical and regression analysis, discovering significant variables for the app usage and revenue generation. Used Twitter API and performed sentiment analysis to understand the customer perception and expectation of Starbucks Mobile ordering app. Provided impactful strategies and recommendations.

● Prescriptive Analysis for Airline Price Optimization

Created models for price optimization using Monte Carlo simulation technique and expectation maximization algorithms factoring in the disruptive changes during pandemic. Published the peer-reviewed results to Acta Scientific.

● Data Analytics to improve Employee Attrition.

Used Kaggle dataset and ML tools to analyze employee data, discovering patterns and correlation among various features related to employee working environment and performance with attrition rate. Formulated recommendations to decrease attrition rate and improvement in employee engagement activities and other HR strategies.

● Analyzing SF City Departments Compensation, Budgeting and Actual Spending for Providing Staffing Recommendations.

Analyzed correlation among factors related to SF city departments, their employee compensation practices, budget allocation, employee standard of living, attrition, and retention. Followed by predictive and prescriptive analysis which covered future budgets based on growth trends of historical data and city population and providing actionable recommendations for staffing.