Customer Service Information Systems

SIDDHI KAMBLE

Jersey City, NJ• **************@*****.*** • +1-917-***-**** • linkedin.com/in/siddhi-kamble/ EDUCATION

Pace University, New York City - MS, Information Systems Sep 2024 - May 2026 St. Xavier’s College, Mumbai - BA, Economics & Sociology (CGPA: 9.09/10) Jun 2018 - Mar 2021 WORK EXPERIENCE

Tech Mahindra Mumbai, India

Senior Analyst, Internal Brand Marketing & Communications Apr 2023 - Mar 2024

● Delivered and presented data-driven reports to clients and the leadership on marketing campaigns utilizing SQL and Excel, resulting in a 17% increase in engagement across 15+ initiatives and contributing over $120,000 to team revenue.

● Evaluated engagement metrics for a large UK-based telecom company to identify trends in customer service, achieving an increase in employee satisfaction and collaboration scores, reducing communication gaps across teams.

● Analyzed large datasets with Google Analytics to optimize customer interactions, collaborating with the Quality & Maintenance team, leading to a 15% reduction in response times, and a 28% improvement in customer satisfaction. Univ.AI - EdTech Startup for Data Science & AI programs Bangalore, India Marketing Associate Aug 2021 - Jun 2022

● Collaborated with a team to manage large datasets (70,000+ student subscribers) to automate audience segmentation and optimize email campaigns, reducing manual handling by 25% through SQL queries and Python scripting.

● Conducted A/B testing with Facebook Ads Manager, utilizing statistical analysis to monitor campaign performance and build automated data pipelines, resulting in a CTR increase and a 12% reduction in cost-per-acquisition.

● Made use of Google Analytics and Microsoft Clarity to analyze website traffic and user behavior. Created dashboards with Tableau to visualize insights, driving a 38% increase in time on page and a 24% reduction in bounce rates.

● Developed custom reports using SQL and Excel to generate insights, leading to a 25% enhancement in customer experience & engagement. Automated reporting processes to improve decision-making efficiency for the business. TECHNICAL SKILLS

● Programming languages – Python, SQL, Scala, R, HTML, CSS, JavaScript

● Databases & Data Warehousing– MongoDB, PostgreSQL, NoSQL, Amazon RDS, Memcached, Teradata, Snowflake, Redis

● Frameworks & Tools–Node.js,Express, React, Flask, Django, Hadoop, Kafka, RabbitMQ, Apache Spark, Hive, Azure, Cloud

● Data Analytics– Statistical Analysis, SAS, Tableau, Power BI, RStudio, Google Analytics, Advanced Excel, A/B Testing

● Add. Skills– ETL Processes, Docker, Azure, AWS, DynamoDB, BigQuery, data modeling, MS Office, Microsoft 365 suite PROJECTS

Movie Recommendation System

● Developed a KNN-based movie recommendation system using Python, Pandas, Scikit-learn, and NumPy.

● Leveraged Apache Spark, Hive, and Hadoop to process large volumes of user interaction data, utilizing distributed computing frameworks to streamline data workflows and enable rapid, real-time updates to the system.

● Produced interactive dashboards with Tableau, seamlessly integrating quality-checked data from SQL databases and Google Analytics to visualize key recommendation metrics and user engagement patterns. Sentiment Analysis: Kamala Harris vs. Donald Trump

● Executed sentiment analysis on 10,000+ social media posts via the Reddit API, using Python libraries (Pandas, NLTK, Seaborn, Transformers). Leveraged Transformer models for public sentiment trends across subreddits.

● Developed visualizations with Matplotlib and Seaborn to categorize sentiments as positive, negative, or neutral.

● Developed and fine-tuned machine learning models using Scikit-learn for sentiment classification, including Logistic Regression, KNN, and Random Forest.

Real-world Trading Simulator

● Built a paper-trading application using Python and Flask, featuring real-time market simulations and integrated data APIs such as Alpha Vantage and Yahoo Finance, PostgreSQL used for database management, and Redis for real-time caching.

● Incorporated Matplotlib, Plotly, and Dash to visualize key trading metrics and generate interactive dashboards, improving user decision-making. Conducted research to determine features that would be better suited for target users. Data Pipeline for Video Games Sales Analysis

● Employed key-value RDDs in Apache Spark to determine the frequency distribution of gaming platforms and perform total sales evaluations for each genre, leveraging tools like PySpark for efficient and scalable data processes & analysis.

● Executed complex join operations with SQL and DataFrames to thoroughly assess the performance of top gaming platforms across multiple regions, ensuring comprehensive analysis.

● Designed and implemented a robust pipeline architecture utilizing Airflow for orchestration, which ensured smooth, continuous data flow throughout the entire analysis process, enhancing workflow efficiency. OTHER ACHIEVEMENTS

Author of Traditionally Published Novel – Isoke (available on Amazon, Barnes & Noble, and other major retailers)

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