Shreyash L
Data Scientist @Morgan Stanley
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WORK HISTORY
Data Scientist @Morgan Stanley
Chicago, IL, US
Transformed Logical Data Model to Erwin, Physical Data Model Foreign Key relationships in PDM, andensured Primary Key, Consistency of definitions of Data Attributes, and Primary Index Considerations. Setup storage and data analysis tools in Amazon Web Services cloud computing infrastructure. Installed and used Caffe Deep Learning Framework. Used Pandas, NumPy, seaborn, SciPy, matplotlib, scikit-learn, NLTK, and spaCy in Python for developingvarious machine learning algorithms.Led data discovery, handling structured and unstructured data, cleaning and performing descriptive analysis,and storing as normalized tables for dashboards. Unearthed raw data by doing Exploratory Data Analysis (Classification, splitting, cross-validation,Regression).Implemented Classification using supervised algorithms like Logistic Regression, Decision trees, KNN, andNaive Bayes.Worked on Hadoop Architecture and various components using HDFS, Job Tracker, Task Tracker, NameNode, Data Node, Secondary Name Node, and Map Reduce concepts.
ABOUT SHREYASH L
Over 8+ years of experience in Machine Learning, Deep Learning, Data Mining with large datasets of Structured and Unstructured Data, Data Acquisition, Data Validation, Predictive Modeling, and Data Visualization.Extensive experience working in various domains like Healthcare, Banking, Service, Retail, and Automobile.Actively Involved in all phases of the data science project life cycle including data extraction, data cleaning,statistical modeling, and data visualization with large data sets of structured and unstructured data.Knowledgeable of Apache Spark and developing data processing and analysis algorithms using Python.Experience in building models with deep learning frameworks like TensorFlow, PyTorch, and Keras.Extensively worked with Python 3.6 (NumPy, Pandas, Matplotlib, NLTK, spaCy, and Scikit - learn)Experienced in Python data manipulation for loading and extraction as well as with python libraries such asmatplotlib, NumPy, SciPy and Pandas for data analysis.Knowledge of Machine Learning algorithms like Classification, Regression, Clustering, Decision Treealgorithms, Random Forests, and Time Series Methods.
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