Machine Learning Engineer Resume

AREA OF INTEREST:

Data Science, Data Analysis, Data Mining and Machine Learning.

COMPUTER SKILLS:

Languages: Python, C++, Java(basic).

Numerical Computing: R, MATLAB.

Systems: Linux, Hadoop.

Database: SQL, MYSQL

Libraries: NumPy, SciPy, Pandas, IPython, matplotlib, scikit - learn, TensorFlow, ggplot2, dplyr, caret, car, randomforest, data.table, XML, jasonlite, RMarkdown, reshape2, twitter, sqldf, MySQLdb, Matcovnet.

WORK EXPERIENCE:

Confidential

Machine Learning Engineer

Responsibilities:

Extract Transfer Load (ETL) data using petl library (python ETL)
AWS lambda function and machine learning services to mine data from web sources using python parser; clean, organize and combine data points to extract useful insights into client profiles
Used nontraditional financial data (crime rate indices, unemployment rates, job security index, average pay,sql job titles based on geolocation) from open source data to formulate a comprehensive outlook of clients’ financial future pay ability to increase account receivables for business
SDLC: operated on weekly sprint facilitated by Agile/Scrum

Confidential

Associate Professional

Responsibilities:

Red Hat 6 and older Linux server infrastructure setup, maintenance and support for virtual and physical hosts.
Managed queues using ServiceNow and Remedy 7; on - call support, SRTs, disaster recovery (DR) testing and datacenter build and migration.
LDAP server/user administration.
Installing and patching of SOE/Applications, TL upgrades, Server tuning etc.
LPAR and HMC Management: Modifying server profile, DLPAR partitions, disk mapping through VIO, etc.
LVM and File System Management: housekeeping of file systems, adding/deleting file systems and modifying size and attributes.
Used shell Scripting for performance monitoring and event tracking.

English Trainer

Confidential

Responsibilities:

Taught beginner and intermediate English to Japanese and Korean speaking populace.
Extracted features using Convolutional Neural Network on Kaggle cats & dogs image dataset using TensorFlow API and Inception-v3 framework, pre-trained on ImageNet dataset
Performance validation for SVM, both linear and kernel SVM Confidential features and varied training samples accompanied by hyper-parameter optimization using Bayesian approach.
Improved two class accuracy to 62% with just 250 samples, 70% for 5000 samples and 63% for third class classification.

Confidential

Data Analyst

Responsibilities:

Evaluated classification performance in a team of three for breast cancer, forest, species, car evaluation dataset from UCI repository using Fisher’s LDA, SVM, LOGREG, KNN, NB, NN, BAG-DT, Kernel-SVM, LASSO on python and R.
Performance analyzed using ROC, LFT, ACC and mean error rates for different training, validation and test cases.
Implemented majorization based LASSO function and Kernel-SVM in Python.

Confidential

Data Analyst

Responsibilities:

Implemented linear regression based model for cancer presence prediction.
Logistic regression based model for classification of cyclic organic compounds in R.
Tested case based datasets for cases involving Confidential for continuous/randomized block design, Latin Squares Design, Factorial Designs, Nested Designs, Split-Plot Designs, Repeated Measures Design and Poisson Regression.

Confidential

Data Analyst

Responsibilities:

Devised beauty product recommendation system using collaborative filtering based on Fitzpatrick skin types.
Implemented collaborative filter in python for 100K users hosted on MYSQL server on Amazon’s RDS
Built website using HTML, CSS and PHP hosted on ec2 instance with LAMP server.

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