Data Scientist Machine Learning

SARA POURNOURBAKHSH

Data Scientist and Machine Learning Engineer

443-***-**** ad5j0x@r.postjobfree.com BOYDS, MARYLAND 20841 linkedin.com/in/sara-pournourbakhsh-804761162/

CAREER OBJECTIVE

A highly qualified Data Scientist and Machine Learning Engineer in different areas including unsupervised learning, supervised learning, reinforcement learning, parameter hyper tuning, KNN, Random Forest, and developing Python in a wide variety of professional technics. Significant experience in data cleaning, data analytics, feature engineering and visualization. Adaptable, passionate, problem solver, attentive to details, takes initiative and possesses and Agile teamwork skills. HIGHLIGHT

• Skills: Python (Various Python Libraries), ML, AI, SQL, NoSQL, MS SQL Server, MySql, MongoDB, Hive, Visualization

• Frameworks: Apache Spark, Hadoop HDFS MapReduce, Tez, PyTorch

• Cloud Skills: AWS

• Tools: Anaconda, Jupiter, MongoDB Compass, MySQL Workbench, Azure Data Studio, Imapla, Tableau, GIT, Docker, VMware, VirtualBox, Excel

• Coursework: Statistics, Business Analytics, Data Visualization, Python Programming, Data Science, Machine Learning: supervised learning (GLM, SVM, KNN, tree-based methods, linear regression and logistic regression), unsupervised learning (clustering, linear and non-linear dimensionality reduction techniques, k-mean), Deep learning, Data Structures & Algorithms. EDUCATION

MASTER OF DATA SCIENTIST AND MACHINE LEARNING ENGINEERING UNIVERSITY OF MARYLAND BALTIMORE COUNTY, UMBE, MD, US January 2024 – Current

PROFESSIONAL CERTIFICATE PROGRAM IN DATA SCIENCE AND BUSINESS ANALYTICS UNIVERSITY OF MARYLAND, COLLEGE PARK, MD, US

March 2022 – March 2023

MASTER OF SCIENCE IN AGRICULTURE ENGINEERING

Azad University, Science and Research Branch

Sep 2013 – Sep 2016

EXPERINCE

Data Scientist AND Mchine Learning Engineer _ Graduate Student Project UMBC - Full time - 2024

Developed a predictive model for assessing the risk of heart attacks. Working with real healthcare data and leveraging cutting-edge machine learning techniques to designing and implementing a robust solution to identify individuals at high risk of cardiovascular events.

- Data Collection and Preprocessing:

Gathered comprehensive datasets containing demographic information, medical history, lifestyle factors, and clinical biomarkers related to heart health. Conducted thorough data cleaning and preprocessing to address missing values, outliers, and inconsistencies.

- Feature Engineering and Selection:

Engineered meaningful features from raw data sources, including age, gender, blood pressure, cholesterol levels, and family history of heart disease. Employed feature selection techniques such as correlation analysis and recursive feature elimination to identify the most predictive variables.

- Model Development and Evaluation:

Implemented state-of-the-art machine learning algorithms such as logistic regression, random forest, and gradient boosting. Fine-tuned hyperparameters using techniques like grid search and Bayesian optimization to optimize model performance. Evaluated model performance metrics including accuracy, precision, recall, and area under the ROC curve (AUC) through cross-validation and holdout validation.

- Interpretability and Explainability:

Employed model interpretation techniques such as SHAP (SHapley Additive exPlanations) values and feature importance analysis to understand the factors contributing to predictions. Generated actionable insights and recommendations for healthcare professionals to intervene effectively and mitigate heart attack risks in high-risk individuals. Data Science Certificate Student. (One Year Specialist Program) University of Maryland 2023

Customer Churn Prediction for Telecom Company

As a Data Scientist and Machine Learning Engineer student works on project for TeleCom Solutions Inc., I spearheaded a critical project focused on predicting customer churn for a leading telecom company. Leveraging advanced analytics techniques and machine learning algorithms.

• Data Preparation and Feature Engineering:

Conducted thorough data cleaning and preprocessing to ensure data quality and consistency. Engineered relevant features from diverse datasets including customer demographics, usage patterns, and service history.

• Exploratory Data Analysis (EDA) and Visualization: Performed comprehensive exploratory data analysis to uncover insights and patterns in customer behavior. Utilized data visualization tools such as Tableau to communicate findings effectively to stakeholders.

• Model Development and Evaluation:

Implemented various machine learning algorithms including logistic regression and random forest.Fine-tuned hyperparameters and optimized model performance using techniques such as cross- validation and grid search.

Evaluated model performance metrics such as accuracy, precision, recall, and F1-score to assess predictive capabilities.

DATA ANALYST

Passargard Electronic payment - Full time

Tehran 2017 – 2021

- Responsible analyze various patient’s EMR and HER to help healthcare provider make clinical decisions.

- Compiled and analyzed business related data to identify insights and areas for improvement decision making. Utilize Tableau for visualization and create interactive graph.

- Worked with primary doctors and development team to implement supervised Machine Learning models.

- Utilized SQL for data extraction and data analysis and import data into Excel and Python notebook to analyze and create data insight.

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