Big data / ETL developer Resume

TECHNICAL SKILLS::

Big data /Data lake components: Hive (HQL), Pig latin, Apache Spark using Scala API, Sqoop, Flume, HDFS, H Catalog, Kafka, Oozie.

Scripting: Data science/Text mining/ Data wrangling with Python libraries - Matplotlib/SciPy/Numpy/Pandas/Seaborn/Scikit/NLTK/Tensor flow/ggplot libraries

Analytical tools: SAS enterprise data miner, Linear programing using Solver, Excel data analysis+

Statistical tools/Packages: R, SPSS, OPENSTATS and STATA

Machine learning/Deep Learning: Neural networks, Gradient Boosted Decision trees, Random forests, Naïve Bayes, Bayesian Networks, K Nearest Neighbor (KNN), Hyper parameter tuning, Regularization and Optimization, Supervised/Un supervised learning, Model & Multiclass evaluation, NLTK tool kit ( Natural language processing), Spark MLib.

Marketing Analytics: Google Analytics, Search engine optimization (SEO), Google AdWords, Campaign management

Data visualization: Tableau (Design, visual analytics, dashboards)

Data base skills: SQL scripts, MySQL DB, Teradata BTEQ scripts

Data Modelling - ERD data models:

No SQL skills- Mongo DB:

Cloud Platform: Google Cloud Platform (GCP)

Operating system: Windows, Linux, Mac OS

Financial tools: Tora trading.

ETL tools: Informatica power center 10.2

Version control/integrated dev env: Github, IntelliJ IDEA, Cran, Jupyter notebook, Filezilla, Putty,Infoworks, Maestro, Serena dimensions.

Domain expertise: Data science, Finance, Retail banking, Mutual funds, Human resource, Digital analytics, marketing analytics.

Machine learning algorithms: Linear regression, logistic regression, neural networks, Decision trees, Random forest, Gradient boosting, Bayesian networks, Naïve Bayes, Principle component analysis, Model selection.

BUSINESS EXPERIENCE:

Confidential

Big data / ETL developer

Confidential

Assistant Manager - Operations and Risk

Confidential

Financial Data Analyst Intern

Confidential Financial Analyst Intern

Responsibilities:

Horton works professional services partner.
Data science/Text mining/ Data wrangling with Python libraries -Matplotlib/SciPy/Numpy/Pandas/Seaborn/Scikit/NLTK/Tensor flow/ggplot libraries
SAS enterprise data miner, Linear programing using Solver, Excel data analysis+
Neural networks, Gradient Boosted Decision trees, Random forests, Naïve Bayes, Bayesian Networks, K Nearest Neighbor (KNN), Hyper parameter tuning, Regularization and Optimization, Supervised/Un supervised learning, Model & Multiclass evaluation, NLTK tool kit ( Natural language processing), Spark MLib.

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