data scientist resume example with 3+ years of experience

Jessica Claire
, , 609 Johnson Ave., 49204, Tulsa, OK 100 Montgomery St. 10th Floor
Home: (555) 432-1000 - Cell: - - - -
Professional Summary
  • Innovative Data scientist/ML engineer with a passion for developing and implementing cutting-edge algorithms and models to drive business solutions.
  • Expertise in designing, training, and deploying machine learning models across various domains, including computer vision, natural language processing, and predictive analytics.
  • Proficient in programming languages such as Python, R, and Java, and familiar with popular frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
  • Strong problem-solving skills, with a track record of developing scalable and efficient solutions for complex business problems.
  • Excellent communication and teamwork skills, with experience collaborating with cross-functional teams to deliver high-quality projects on time and within budget.
  • Data analysis
  • Machine learning
  • Data visualization
  • Statistical analysis
  • Big data technologies
  • Programming languages (Python, R)
  • Data engineering
  • Natural language processing
  • Cloud computing
  • Data mining
Work History
07/2021 to Current
Data Scientist Leidos Germantown, MD,

Project - Fraud Detection


  • Worked with the fraud detection team to identify key data sources and design data collection processes to ensure accurate and complete data collection.
  • Conducted exploratory data analysis (EDA) on large financial datasets to identify patterns, anomalies, and correlations that may indicate fraudulent activities.
  • Developed and implemented predictive models using statistical techniques (ogistic regression, decision trees, random forests) to identify potentially fraudulent transactions and patterns.
  • Built and optimized anomaly detection models using techniques such as clustering or Gaussian mixture models to detect unusual behavior in client or account data.
  • Utilized machine learning techniques (neural networks, gradient boosting, or deep learning) to improve model performance and reduce false positive or false negative rates.
  • Developed and implemented real-time fraud detection algorithms to monitor transactions and identify potentially fraudulent activities in real-time.
  • Worked with cross-functional teams to integrate the fraud detection models into the company's workflow, and ensured adherence to regulatory requirements.
  • Conducted post-mortem analysis of fraudulent cases to identify patterns and improve the accuracy and timeliness of the fraud detection models.
  • Developed dashboards and visualizations to communicate the results of fraud detection models to stakeholders and senior management.
  • Continuously monitored and evaluated the performance of the models, and iteratively improved them based on feedback from stakeholders and the latest research in the field.
07/2020 to 06/2021
Machine Learning Engineer Booz Allen Hamilton Inc. Knoxville, TN,

Project - Sentiment Analysis


  • Conducted sentiment analysis on a dataset of customer reviews using natural language processing techniques.
  • Pre-processed the data by cleaning and tokenizing the text, removing stop words, and performing stemming/lemmatization.
  • Developed and trained a machine learning model (e.g. SVM, Naive Bayes, LSTM) to predict the sentiment of the reviews.
  • Fine-tuned the model by experimenting with different feature extraction techniques, hyperparameters, and evaluation metrics.
  • Achieved an accuracy of 84% on a test set, outperforming previous state-of-the-art models in the field.
  • Visualized the results using charts, word clouds, and other data visualization tools to gain insights into the sentiment distribution.
  • Deployed the model to a web application using a Flask or Django framework, allowing users to input their own text and receive a sentiment score.
  • Collaborated with a team of data scientists and software engineers to integrate the sentiment analysis model with other systems and APIs.
  • Documented the project in a clear and concise manner, including the problem statement, methodology, results, and future directions.
  • Presented the project to stakeholders and received positive feedback for its potential business value and impact on customer satisfaction.
04/2017 to 06/2018
Junior Data Scientist Deloitte Riverside, CA,

Project - Sales Prediction


  • Developed and implemented machine learning algorithms to optimize the company's product recommendation system, resulting in a 10% increase in online sales.
  • Conducted exploratory data analysis to identify customer preferences and market trends, providing insights for the design and production teams to develop new product lines.
  • Built and maintained databases to store and organize customer and sales data, improving data accessibility and accuracy for the entire organization.
  • Collaborated with cross-functional teams to develop and implement data-driven solutions for business problems, such as inventory optimization and sales forecasting.
  • Conducted A/B tests to evaluate the effectiveness of different marketing strategies and promotions, providing recommendations for optimizing marketing campaigns.
  • Implemented data privacy and security measures to ensure compliance with industry regulations and protect customer information.
  • Stayed up-to-date with the latest industry trends and advancements in machine learning and data science, attending conferences and participating in online learning communities.
Expected in 05/2020 to to
Master of Science: Computer Science
Pace University - New York City,
  • 3.66 GPA
Expected in 04/2018 to to
Bachelor of Technology: Electronics & Communications
Jawaharlal Nehru Technological University Kakinada - India,
  • 3.2 GPA

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Resume Overview

School Attended

  • Pace University
  • Jawaharlal Nehru Technological University Kakinada

Job Titles Held:

  • Data Scientist
  • Machine Learning Engineer
  • Junior Data Scientist


  • Master of Science
  • Bachelor of Technology

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