- Around 5 years of experience in Python Programming/ Machine Learning/ Computer vision/ Probabilistic Graphical Models/ Inferential statistics/ Graph Theory/ System Design.
- Hands on experience with 3 years on Machine Learning Statistic Modeling, Predictive Modeling, Data Analytics, Data Modeling, Data Architecture, Data Analysis, Data Mining, Text Mining and Natural Language Processing (NLP), Artificial Intelligence algorithms.
- Part of R&D team to build new analytics POC's using Apache Spark, Scala, Python and machine Learning .
- Proficient to understand of Spark core , Spark SQL , Spark Streaming and Spark MLlib .
- Regression analysis , Statistical test analysis , Report and Dashboard generation , Data management .
- Python , Numpy , Scikit - Learn , genism , NLTK , Tensorflow , keras .
- Experience in Machine Learning, Statistics , Regression - Linear , Logistic , Poisson , Binomial .
- Experience building solutions for enterprises, context-awareness, pervasive computing, and/or application of machine learning
- Research and development of machine learning pipeline design for Optical Character Recognition (Handwritten), anomaly detection system using multi variate Gaussian model.
- KBC, Chatbots, Adaptive Supervised Learning (deterministic classification), Unsupervised Learning methods for IE, ANN and DeepNN for NLP and Chatbots, Probabilistic models for NLG and inferences, Decision science.
- Hands on experience in data mining algorithms and approach.
- Good at algorithm and design techniques.
- Strong programming expertise Python and strong in Database SQL.
- Solid coding and engineering skills in Machine Learning
- Proficient in Python, experience building, and product ionizing end-to-end systems
- Knowledge of Information Extraction, NLP algorithms coupled with Deep Learning
- Experience with file systems, server architectures, databases, SQL.
Programming, Scripting Languages and other Skills: Python (numpy, scipy, scikit-learn, nlkt, gensim, keras), Scala, Spark, spark streaming, Machine Learning, Deep Learning, C, HTML.
Databases: SQL Server 2014/2012/2008/2005/2000 , MS-Access, Oracle 12c/11g/10g/9i
Python Programming Skills / Web Frameworks: Tensorflow, Keras, NLTK, Scipy, Pyspark, pandas, numpy, Plotly, Seaborn, Matplotlib, Scikitlearn, Data Preprocessing, Web Scraping, Data Extraction, Django, Open CV.
Statistical Methods: Time Series, regression models, splines, confidence intervals, principal component analysis and Dimensionality Reduction, bootstrapping.
Supervised and Unsupervised: Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks (LSTM), Sentiment Analysis, Computer Vision, Keras, Tensorflow, Pytorch.
Tools and Utilities: SQL Server Management Studio, SQL Server Enterprise Manager, SQL Server Profiler, Microsoft Office, Excel Power Pivot, Excel Data Explorer, IBM Watson, Spark MLlib.
Machine Learning: Classification, KNN, Regression, Random Forest, Clustering(K-means), Neural Networks, SVM, Bayesian Algorithm, Sentimental analysis, Market Base Analysis, Bagging, Boosting.
Confidential - Frisco, TX
Machine Learning Engineer
Roles and Responsibilities:
- Experience with TensorFlow, Theano, Keras and other Deep Learning Frameworks.
- Built Artificial Neural Network using TensorFlow in Python to identify the Hiring and performance probability of Working Professionals.
- Understanding the business problems and analyzing the data by using appropriate Statistical models to generate insights.
- Predicted the products that are prone to be back ordered and products that are expected to be canceled.
- Data cleansing, transformation and creating new variables using pandas.
- Data validation through cleaning and scaling the required variables.
- Wrote Python routines to log into the websites and fetch data for selected options.
- Wrote scripts, front end configuration and code in Python, CSS.
- Worked on different data formats such as JSON, XML and performed Machine Learning algorithms in Python.
- Analyzed large data sets apply Machine Learning techniques and develop predictive models, statistical models and developing and enhancing statistical models by leveraging best-in-class modeling techniques.
- Building the back end of an automated Machine Learning platform that allows clients to fully leverage their data without building out a traditional analytics team.
- Generalized feature extraction in the Machine Learning pipeline, reducing onboarding time for new customers from days to hours
- Designed and prototyped a Machine Learning system for identifying experts/non-experts for a given topic through iterative feature engineering and model development.
Environment: Python, Pandas, Scikitlearn, Regression, Classification, CNN, RNN, Random Forest, Tensorflow, Keras, Seaborn, Numpy, SVM, Preprocessing, Sql.
Machine Learning Engineer
Roles and Responsibilities:
- Performed data analysis, visualization, feature extraction, feature selection, feature engineering using python pandas, numpy, seaborn etc.
- Developed scalable Machine Learning solutions within a distributed computation framework.
- Applied Spark RDD's transformations and actions on raw data.
- Establish scalable efficient automated processes for large scale data analyses model development model validation and model implementation.
- Implemented Word2Vec for generating the vectors of words in TensorFlow.
- Developed Name Entity Recognition using Bi Directional Long Short Term Memory (LSTM)
- Implemented SparkMlib utilities such as including classification, regression, clustering, collaborative filtering and dimensionality reduction.
- Utilized Convolution Neural Networks to implement a Machine Learning image recognition component using TensorFlow.
- Implemented Back-propagation in generating accurate predictions.
- Utilized NLP applications such as topic models and sentiment analysis to identify trends and patterns within massive datasets.
- Avoided overfitting by following standard practices such as keeping the number of independent parameters less than the data points avoidable in the model.
- Loaded data from Hadoop and made it available for modeling in Keras.
- Prepared multi-class classification data for modeling using one hot encoding.
- Used Keras neural network models with Apache Spark.
- Enhanced model performance by calibrating parameters, researching and improving optimization and weights initialization methods.
- Worked closely with internal stakeholders such as business teams, product managers, engineering teams and partner teams.
Environment: Python, Pandas, Scikitlearn, Regression, Classification, CNN, Random Forest, Tensorflow, Keras, Seaborn, Numpy, Preprocessing.
Roles and Responsibilities:
- Utilized Python libraries wxPython, NumPy, Pandas, and matPlotLib.
- Collaborated with data engineers and operation team to collect data from internal system to fit the analytical requirements.
- Implemented SQL Alchemy which is a python library for complete access over SQL.
- Worked on Element Tree XML API in python to parse XML documents and load the data in database.
- Skilled in collections and used for manipulating and looping through different user defined objects.
- Worked on data cleaning, data preparation and feature engineering with Python.
- Used Pandas library for statistical Analysis.
- Pandas library was used for flexible reshaping and pivoting of data sets.
- Performed Exploratory Data Analysis and Data Visualizations using Python .
- Used Principal Component Analysis in feature engineering to analyze high dimensional data.
- Created entire application using Python, Django, MySQL and Linux.
- Installed, configured, and managed the AWS server.
- AWS data pipeline for Data Extraction, Transformation and Loading from the homogeneous or heterogeneous data sources.
- Accessed database objects using Django Database APIs.
- Worked on python based test frameworks and test driven development with automation tools.
- Collaborated with team members and translated functional requirements to technical requirements for development.
- Wrote automation test cases and fixing automation script bugs.