Data Analyst, Marketing analytics Resume

RELEVANT SKILLS:

Data Mining: Probability, Machine Learning, Statistics, Regression, Deep Learning, Decision Tree, Random Forest, Bayesian Inference, Supervised and Unsupervised learning, Predictive Modeling

Tools: R, SQL, JMP, SAS Enterprise, Python, Excel

PROFESSIONAL EXPERIENCE:

Confidential

Data Analyst, Marketing analytics

Responsibilities:

Conducted A/B testing and used statistical tools such as google analytics to understand customer behavior
Increased overall traffic to website by 65% using SEO, social media analytics & data science
Created and maintained social media profiles

Confidential

Data Analyst, Marketing analytics

Responsibilities:

Cleaned, pre - processed and explored the data to find hidden patterns in the customer behavior
Used machine learning algorithms to find out the claim cost for each policy of the company
Applied text pre-processing and normalization techniques such as tokenization and parsing
Performed feature selection and feature engineering to reduce the dimension of highly unstructured data
AutoSummarized the articles in three sentences to get quick summary of articles
Classified an article as Tech or Non-Tech using supervised algorithms such as Naïve Bayes
Reduced dimension of very large and highly unstructured data using SVD
Performed feature Engineering and used Word2Vec features to enhance the performance of models
Used different performance metrics- Levenshtein distance, cosine similarity etc. to measure similarity
Achieved a good accuracy by using ensemble model
Preprocessed data by removing outliers, imputing missing values and transforming variables
Used 2 level classification model to deal with highly skewed data and fulfill different business needs
Used different algorithms such as Logistic, Random Forest and Boosted Tree to make an ensemble model
Recommended Top 5 destinations for every new user to help customize the marketing approach
Applied Feature Creation & Selection, Missing data imputation & Outlier detection to preprocess the data
Developed multi-class classification predictive models using machine learning algorithms
Accurately classified Prudential Life Insurance customers based on risk and eligibility of insurance
Used statistical natural language processing to mine unstructured data and find insights
Utilized Python packages to preprocess and parse the text and used regular expressions to find the patterns

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