Machine Learning Engineer Resume

SUMMARY

7 years of experience in Data Science, Machine Learning and Big Data Analytics with proficient understanding of Machine Learning technologies, Algorithms and Statistical Methods

TECHNICAL SKILLS

Languages/Methodologies: Python, R, JSON, XML, Basics of Scala

Technologies: Machine Learning, Sklearn (scikit - learn), PySpark, Keras, NumPy, Pandas, Matplotlib, NLP, NLTK, AWS, Azure, Hadoop, Map/Reduce, BigQuery, Kafka, XML, DTD, Kubernetes, OpenCV

Software tools and Utilities: Tableau, Jupyter notebook, Spider, Google Colab, IntelliJ IDEA, Sci2

Database Technologies: MySQL, MongoDB, HBase

Build & Resource Management: Maven, Git, JIRA, Gradle

Operating Systems: UNIX, Linux

PROFESSIONAL EXPERIENCE

Confidential

Machine Learning Engineer

Responsibilities:

Part of Research and Development team who develops Virtual Agent based technologies.
Identify various design and functional problems and possible feature additions
Applying research and developing the solutions and add it to the product platform with the team of engineers
Apply machine learning algorithms and technologies to train the platform for NLP

Technologies: Python, Keras, Tensorflow, OpenCV, Pillow, NLP, Scikit-Learn, Kubernetes

Confidential, Bloomington

Machine Learning Engineer

Responsibilities:

Analyzed the global commodity trade to show some interesting trade flows and statistics like top countries involved and top items trade
Costa Rican Household Poverty Level Prediction is done for a social cause to help Confidential Development Bank to identify which households have the highest need for social welfare assistance.
Used Sentimental analysis to identify the product rating.
Implemented a multi-dimensional visualization dashboard on Tableau software, which narrates a story commencing with the inception of Client, its growth, geolocation and value analysis, statistical analysis of data, pareto analysis and other significant findings
Exploratory Data Analysis (EDA) and Data Preprocessing to standardize and transform the data. Different data transformation techniques like OHE, scaling and normalization for better ML modeling
Tableau Visualizations, dashboards and storyboards to convey business insights
Feature engineering to understand model significant features using Selectkbest algorithm and PCA.
Combined ML Pipelines for categorical and numerical data transformation, feature engineering and modeling.
Created Baseline models
Created ML models of logistic regression, decision tree, random forest, SVM, GBDT, Bayesian model, Deep learning Logistic regression model using Keras, hard and soft ensemble models
Model comparison and carried out significance tests to identify best models
Prediction using the best ML model
Text Processing
ML Pipelines
Sentiment Analysis and Prediction on Tweet data

Technologies: Python, SQL, PySpark, Pymongo, BigQuery, Scikit-Learn, NO-SQL, Tableau, Pandas, SKlearn, Keras, Jupyter notebook, Google Colab, Matplotlib, Pandas, NumPy, NLP, Tweepy, Matplotlib, Textblob, Nltk, Seaborn, Pandas, Afinn, Scipy, R

Confidential

Data Analyst

Responsibilities:

Cleansing, Data Validation and Data Governance on large data set in a distributed environment.
Classified customers into different financial categories using multinomial logistic regression, random forest, decision trees to help targeted campaigns
Different data transformation techniques like OHE, scaling and normalization for better ML modeling
ML pipelines for data transformation, modeling and prediction
R for Statistical Analysis to help data exploration and visualization
Pandas UDF in PySpark in a distributed environment to solve the scaling problems
Data manipulation was done on both compressed(Parquet) and uncompressed (CSV) dataset in HDFS.
Visualized the data and insights using Matplotlib and Tableau dashboard and storyboards
Communicated the results to business teams for taking best decisions.

Technologies: Python3, Hadoop HDFS, PySpark, R, Tableau, SQL, Map/Reduce, Kafka, Matplotlib, scikit-learn

Confidential

Team Member /Team Lead/Transition Manager

Responsibilities:

Take complete ownership as a project manager of all the accounts from initiation to handover to operations
Transitioned more than 8 global clients into Cognizant Global Shared Services Centre.
Basic troubleshooting of Cisco and Juniper network devices

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