Free Data Scientist Resume Sample [2022]

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6+ years experienced data scientist with a passion to solve real-world business challenges using data analytics. Track record of setting up the Data Science Div. for a leading hospitality firm & rendering consultancy services for a Fortune 500 company. Proficient in deploying complex machine learning and statistical modeling algorithms/techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.
  • Packages: SciKit-Learn, NumPy, SciPy,, Pandas, NLTK, Beautiful Soup, Matplotlib, StatsModels
  • Big Data Stack: Hadoop, Apache, Pig, Python, PostgreSQL, AWS, Hive, MongoDB, MapReduce, Spark, Linux
  • Statistics/ML: Linear/Logistic Regression, SVM, Ensemble Trees, Random Forests, Clustering, Gradient Boosted trees

Data Analysis

Stakeholder Management

Leadership & Training


Project Management & Delivery

Process Improvement

Team Incubation

Data Visualization

Predictive Modelling & Analytics

Sentiment Analysis

  • Awarded the “Best Employee Award" | Positronix Financial Services, '17
  • Received the 'CEO Appreciation Award' | Epiplace Solutions, '16
  • Certified 'Machine Learning Expert', OpenAI, '17
  • Certified 'Expert Data Scientist', Stanford University, '16
  • Speaker, Open Data Science Conference, San Francisco, '16
  • Published: “Modern methods of dynamic pricing for hotels”, The Data Science Journal, '15
    Technology Stack: Python, Hadoop, AWS, Pandas, NumPy, SciKit-Learn,
    Data Visualization & Predictive Analytics
    • Steering rapid model creation in Python using Pandas, NumPy, SciKit-Learn & for data visualization
    • Constituting NLP models for Sentiment Analysis & MapReduce modules for predictive analytics in Hadoop on AWS
    • Unfurled ridge regression model & LASSO solver via gradient descent to select the regularization parameters
    • Designing real-time contextual behavioral personalization system via econometric & ML to predict customer behavior
    Statistical Modeling & ML Algorithms
    • Placed various machine learning techniques to build dynamic pricing models and maximize profitability
    • Led the development of a performance assessment & pricing analysis platform via k-NN Algorithm
    • Formed multivariate regression based attribution models using ad stock analysis from the digital marketing data
    • Generated segmentation models using K-means Clustering in order to discover new segments of users
    Key Achievements
    • Established the Data Science division from scratch by recruiting, on-boarding & training a team of 8 Data Analysts
    • Formulated clustering & regression analysis to resolve a shipping consolidation issue & reduce costs by USD 3 million
    • Successful in an overall loss reduction of 10% on monthly revenue by implementing the loss minimization techniques
    • Migrated data transformation processes on Hadoop to reduce data processing time by 25% & cut costs by USD 550k
    • Developed a customer segmentation algorithm using Python to boost sales leads & increase market share by 28%
      Technology Stack: Python, Pandas, NumPy, SciKit-Learn, Matplotlib, Jupyter Notebook
      Segmentation & Clustering
      • Applied large scale & low latency machine learning for non-parametric models & high-dimensional data visualization
      • Created multivariate regression-based attribution models & segmentation models using K-means Clustering
      • Utilized high dimensional data sets from users/media agencies/3rd-party apps via PCA, LDA & Kernel Approximations
      Key Achievements
      • Developed an additive scoring model for QSM and a logistic regression model to yield a K-S statistic of 51.5
      • Deployed SGD, Logistic Regression, Random Forest, SVM, etc. for classification models to boost avg. click rate by 34%
        Data Analytics & Model Development
        • Directed model development, validation, testing and implementation of analytical products and applications
        • Developed an additive scoring model for QSM & a logistic regression model which yielded a K- S statistic of 51.5
        ML Algorithms & Statistical Analysis
        • Stationed advanced text mining algorithms to identify search intent latent in individual keywords
        • Tested and implemented decision trees, random forests and ensemble models using bagging and boosting
        • Employed Principle Component Analysis to analyze collinearity and reduce the dimensionality of the dataset
        SalesData VisualizationPredictive ModellingSentiment AnalysisStakeholder Management
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          • Languages: English, Spanish and Catalan
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