Jay Singh - SDE-II (Data Science)

Mail | LinkedIn | GitHub

Summary

  • Experienced data scientist with a strong background in Python, machine learning, and data analysis. Passionate about deriving insights from complex data and implementing solutions to solve real-world problems.

Education

Master of Computer Applications

Gujarat Technological University - Graduated 2019

Bachelor of Computer Applications

Gujarat University - Graduated 2015

Skillsss

  • Programming: Python, R
  • Machine Learning: Scikit-learn, TensorFlow, PyTorch
  • Data Analysis: Pandas, NumPy
  • Data Visualization: Matplotlib, Seaborn, Plotly
  • SQL and Database Management
  • Natural Language Processing (NLP)
  • Version Control: Git

Experience

Data Scientist | Company XYZ

Jan 20XX - Present

  • Developed predictive models to improve customer retention, resulting in a 15% reduction in churn.
  • Collaborated with cross-functional teams to define business objectives and deliver actionable insights.
  • Implemented NLP techniques to analyze customer feedback, leading to improvements in product features.

Machine Learning Engineer Intern | Company ABC

Jun 20XX - Aug 20XX

  • Built a recommendation system using collaborative filtering, improving user engagement by 20%.
  • Conducted exploratory data analysis to identify patterns and anomalies in large datasets.
  • Created interactive data visualizations to communicate insights to non-technical stakeholders.

Projects

Customer Segmentation Using K-Means Clustering

  • Applied K-Means clustering to segment customers based on their purchase behavior.
  • Visualized the results using scatter plots and silhouette analysis.

Sentiment Analysis of Twitter Data

  • Used natural language processing techniques to analyze sentiments in Twitter data.
  • Created word clouds and sentiment histograms to visualize the results.

Certifications

Publications

  • Doe, J., Smith, A. (20XX). “Predictive Modeling for Customer Churn in Subscription Services.” Journal of Data Science, 10(2), 123-135.

References

Available upon request.