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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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