Data Scientist Resume SF Bay Area - Hire IT People

SUMMARY

Data Scientist with over 6+ years of experience in Python and Machine Learning. Recognized consistently for performance excellence and contributions to success in the fin - tech and agro-tech industry.
Strengths include working with teams in arriving at critical conclusions and taking data-driven decisions.

TECHNICAL SKILLS

Data Wrangling
Data analysis
Visualization
Modelling using Statistics
Python
Numpy
Pandas
SQL
MS Excel
Tableau
Machine Learning Algorithms
NLTK
Tensor Flow
PyTorch

PROFESSIONAL EXPERIENCE

Data Scientist

Confidential, SF Bay Area

Responsibilities:

Participated in all phases of project life including data collection, in the development, validation and delivery of algorithms, statistical models and reports creating. Solved complex analytical problems.
Involved in team meetings, discussions with business teams to understand the business use cases.
Performed EDA to take care of missing values, handling Categorical features, Handling imbalance data set and feature selection by using Python.
Used the Supervised and unsupervised techniques such as Logistic Regression Classifier, Random Forest Classifier, Auto encoder neural networks, DBSCAN, Isolation Forest, Local Outlier Factor, Elliptic Envelope, One-Class Support Vector Machines to classify the providers in Fraud and Non-Fraud category.
Found the model Performance, based on prediction probabilities, Recall and Precision tradeoff and F1 score.
Used computer vision such as object detection, image classification, image anomaly detection of CT scan and MRI image was done using CNN, Mask R-CNN(Mask Region-based Convolution Neural Network) model, Keras and TensorFlow.
Computer vision API was used for Optical Character Recognition (OCR) to extract text digits and integers from images and PDF documents.
Natural Language Processing (NLP) such as sentiment analysis, entity recognition, Topic Modeling and Text summarization was done using advanced python library such as NLTK, TextBlob, Spacy and Gensim.
Design, built and deployed a set of python modeling APIs for customer analytics, which integrate multiple machine learning techniques for varioususer behavior pattern prediction and support multiple marketing segmentation programs
Segmented the customers based on demographics, geographic, behavioral, and psychographic data using K-means Clustering. Designed and implemented end-to-end systems forDataAnalytics and Automation, integrating custom visualization tools using Python and Tableau.
Machine Learning was used for building, training, and deploying machine learning models faster using drag-and-drop designer and automated machine learning.
Rapidly build and deploy machine learning models with the no code designer and visual machine learning studio, built-in collaborative Jupyter notebooks to accelerate model creation with the automated machine learning, and access built-in feature engineering, algorithm selection, and hyper parameter sweeping to develop highly accurate models.
Involved in agile environment to implement agile management ideals such as sprint planning, daily standups, managing project timelines, and communicate with clients to ensure project progress satisfactorily.
Extensively used Power BI, Pivot Table and Tableau to manipulate large data and develop visualization dashboard.
Apache Spark was used for bigdata processing, streaming, SQL, Machine Learning (ML), Exploratory Data Analysis (EDA) and feature extraction.
Pandas Data frame, NumPy, Jupyter Notebook, SciPy, scikit-learn, TensorFlow, Keras, and Theano was used as a tool for Machine Learning and Deep Learning.
Wrote complex SQL statements to interact with the RDBMS database to filter the data and data analytics.
Developed ETL processes in Python to send large data sets into Hadoop and bring summarized results back into the SQL data warehouse.

Data Scientist / Machine Learning Engineer

Confidential

Responsibilities:

Design machine learning models from POC’s, apply algorithms to generate accurate predictions.
Ensuring that algorithms generate accurate user recommendations and information for reporting.
Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.
Developing ML algorithms to analyze huge volumes of historical data to make predictions.
Running tests and interpreting test results. To identify differences in data distribution that could affect model performance.
Documenting machine learning processes and generate reports for the business users.

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Data Scientist Resume

Sf Bay, AreA

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