machine learning intern resume example with 1+ years of experience

(555) 432-1000,
, , 100 Montgomery St. 10th Floor

Data Science enthusiast with strong analytical and problem-solving capabilities, skilled at manipulating large-scale datasets and building machine learning models to provide effective data solutions and assessments with practical experience of using Python, R, SQL, Tableau and Power BI, and collaborative ability to further develop a technical career in the field of Data Science.

  • Languages: Python, R, SQL
  • Databases: Oracle SQL Developer, MS Access, Microsoft SQL Server Management Studio, MongoDB, Spark
  • Machine Learning: Regression, Classification, Association, CART, Clustering, Boosting, CNN, RNN, LSTM, MLP
  • Visualization Tools: Tableau, Power BI, MS Excel
  • Data Management: Database Design, Exploratory Data Analysis, Data Warehousing, Feature Engineering
  • Data Science Libraries: NumPy, Pandas, Matplotlib, Scikit Learn, SciPy, Keras, TensorFlow, OpenCV
Education and Training
George Mason University Fairfax, VA, Expected in 05/2021 Master of Science : Data Analytics Engineering - GPA :

GPA: 3.90

Jawaharlal Nehru Technological University Hyderabad, India, Expected in 05/2019 Bachelor of Technology : Electronics and Communication Engineering - GPA :

GPA: 3.70

Brunswick Corp. - Machine Learning Intern
Naples, FL, 12/2020 - 04/2021
  • Developed a Handwritten Text Recognition model that converts the handwriting in an image to machine-readable text using a CNN-LSTM model which is trained on 20,000 images for 1 million iterations on Momentum AI Server.
  • Performed hyper-parameter tuning with CTC loss function and the training parameter being error rate to achieve a character error rate of 9.58 and a word error rate of 19.35 by validating the model on 4000 images.
  • Prepared Gantt charts using Microsoft Project and used YouTrack agile boards to plan, track and manage 5 sprints.
Jefferson Health - Graduate Assistant
Montgomery County, PA, 07/2020 - 04/2021
  • Served as a Teaching Assistant for Natural Language Processing course, held weekly office hours where students get help on the assignments and course concepts such as Text wrangling, n-grams, POS tagging, Regex, Statistical Language Modeling and Sentiment Analysis.
  • Evaluated students’ performance on coding assignments, midterms and final exams.
  • Worked as a Research Assistant and Performed data cleaning, Imputation, correlation of the parameters, outlier detection of the Singapore Airbnb data and presented a unique perspective of the data with the help of geographical maps.
  • Developed classification models by implementing Decision Tree, Random Forest to find the node purity and using 10–fold cross-validation to report the RMSE & Accuracy of models to classify and predict the prices in different neighborhoods and performed k-means clustering to generate price clusters in the geographical map.
Align Technology - Data Engineer Intern
Woburn, MA, 04/2020 - 11/2020
  • Built an ETL pipeline to load 0.1 million patient health records from various parquet files, perform preprocessing steps such as cleaning, joining, filtering and aggregating using PostgreSQL before transforming the data into FHIR patient and encounter resources which are loaded into Spark data frames to implement distributed processing.
  • Carried out feature engineering to develop an MLP model that classifies the emotions into 7 classes from speech signals.
  • Organized an AWS Deep Racer workshop, trained and mentored 50 participants on the usage of the AWS platform for developing a Reinforcement learning model to participate in the virtual racing community leagues.
Brunswick Corp. - Machine Learning Intern
Nashua, NH, 04/2018 - 08/2018
  • Developed an OpenCV model that uses Haar cascade classifier to detect and track the speed of vehicles in the video frames with the help of Dlib object detection and achieved an accuracy of 82% by comparing real speeds of 100 vehicles.
  • Performed outlier detection, imputation, correlation analysis, Label encoding and created dashboards in Power BI.
  • Developed and compared Logistic Regression, Random Forest Classifier and XG Boost classifier where the latter being the best model with the highest accuracy of 88% to predict whether a person takes the vehicle insurance or not.
  • Used Repeated 10–fold cross-validation to report the accuracy, RMSE and AUC values of the models and found that Vehicle damage is the important predictor by using the Feature Importance plot.
  • ACADEMIC PROJECTS ( Emotion Recognition using vocal sounds Python.
  • Performed feature identification and extracted MFCC’s by passing the audio signals through the Mel filter bank and horizontally stacking them with the frequencies of the audios converted to Mel Scale using Librosa.
  • Designed an MLP model that classifies calm, happy, fearful, neutral and sad emotions with the precision of every emotion being greater than 0.80 and overall accuracy of 85% by taking .wav files as input.
  • Language Translation Pipeline Python.
  • Developed a Bi-directional Recurrent Neural Network with a Repeat vector and a Dense layer that takes a French sentence as input which is padded during pre-processing, translates it to an English sentence and vice-versa.
  • Achieved a Bilingual Evaluation Study Score of 0.5 when tested on 10,000 sentences which is about 10% increase compared to a Long Short-Term Memory-based encoder-decoder that uses sequential modeling.

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Resume Overview

School Attended

  • George Mason University
  • Jawaharlal Nehru Technological University

Job Titles Held:

  • Machine Learning Intern
  • Graduate Assistant
  • Data Engineer Intern
  • Machine Learning Intern


  • Master of Science
  • Bachelor of Technology

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