Data Analyst

Kasra Gordini

**********@*****.*** 201-***-**** LinkedIn

PROFESSIONAL EXPERIENCE

Data Product Analyst June 2024 – Present

AmikoXR (InsightsGen) Hillsborough, NJ

Led the integration of long-term memory, short-term memory, and Retrieval-Augmented Generation (RAG) to enhance AI agents' personalized responses and optimize performance.

• Enhanced data products by integrating long-term memory into AI agents using Neo4j, Qdrant, and memory- augmented models APIs, achieving a 900 ms reduction in response time and nearly 20% contextual accuracy.

• Optimized AI agent's short-term memory using a caching layer and Least Recently Used (LRU) eviction policy, improving AI agent response times from 880 ms to 740 ms. Data Analyst Intern Jul 2023 – Jan 2024

Stevens Institute of Technology Hoboken, NJ

Worked on improving data accuracy and retrieval speed by cleaning and transforming data, involved in:

• Optimized database performance by refining indexing strategies and making configuration adjustments, resulting in faster query execution times.

• Implemented ETL from multiple sources, optimizing pipelines with parallel processing and incremental loading, reducing processing times by 30%.

Business Development Manager Jul 2017 – Sep 2021

Kia Green Tehran, Iran

Promoted to lead the team with the Middle East responsibility in 2018, overseeing sales and marketing. Architected statistical models for demand forecasting and inventory optimization, driving a $6 million sales increase from 2018 to 2021. Involved in:

• Conducting regression analysis and time-series modeling to forecast trends and optimize inventory turnover.

• Analyzing large datasets to identify demand patterns and optimize procurement cycles, reducing stockouts by 16% and improving supply chain efficiency.

Business Analyst Jul 2014 – Sep 2017

Kia Green Tehran, Iran

Generated $4 M in revenue growth from 2014 to 2017, with 12% average annual growth, involved in:

• Designed automated KPI dashboards in SQL and Tableau for real-time tracking, enabling data-driven decisions.

• Employed data visualization techniques to analyze trends, facilitating exploratory data analysis. EDUCATION

Stevens Institute of Technology Jan 2022 – Jan 2024 MS in Business Intelligence and Analytics (AI Concentration) University of Science and Technology Feb 2015 – Feb 2020 BS in Business Administration

SKILLS AND EXPERTISE

• Python, SQL, R, SAS

• Anaconda, Google Colab, Jupyter Notebook, Git, Microsoft Suit, Tableau, Power BI, Alteryx, LaTeX

• Relational DB, Neo4j Graph DB, Mongo DB, Apache Cassandra, Snowflake

• ETL, NLP, Data Wrangling, Data Visualization, Statistical Analysis, Hypothesis Testing

• Pandas, Numpy, Matplotlib, NLTK, Scipy, StatsModels, Scikit-learn, TensorFlow, PyTorch, Keras

• Linear and Logistic Regression, Classification, Decision Trees, Clustering, Neural Network PROJECTS

AI Agent Chatbot Development (LangChain, Qdrant, APIs)

• Designed and deployed a high-performance chatbot leveraging LangChain, Qdrant, LLM, and RAG to enhance data retrieval, AI knowledge base, and response accuracy for AmikoXR. [GitHub] Sales Data Analysis and Demand Forecasting (Tableau, SQL)

• Used SQL to extract, preprocess, and aggregate historical sales data, identifying trends for demand forecasting.

• Created dynamic Tableau dashboards to visualize sales trends and forecasts for the Kia Green strategic insights. Classification (Python, Scikit-learn, Seaborn)

• Leveraged diverse machine learning algorithms and comprehensive visualizations to predict satisfaction from behavioral responses. [GitHub]

Sentimental Analysis in X (TensorFlow, NLP, APIs)

• Built a logistic regression and LSTM units using TensorFlow for sentimental analysis in X.

• Preprocessed text data with tokenization and embedding layers, achieving 77% accuracy in sentiment classification. [GitHub]

Clustering (Python, Scikit-learn, Clustering)

• Built a clustering model to perform unsupervised learning for customer segmentation based on purchasing behavior based on a startup business dataset.

• Employed K-Means to derive customer clusters using feature extraction and dimensionality reduction.

• Conducted exploratory data analysis (EDA) for data preprocessing, including normalization and encoding. Data Warehouse & BI System (PostgreSQL, Tableau) Designed and implemented a scalable star-schema data warehouse for US Superstore using PostgreSQL and Tableau:

• Leveraged SQL, window functions, and materialized views for scalable sales and inventory data management.

• Integrated Tableau for comprehensive data visualization, enabling real-time OLAP reporting, advanced KPI analysis, and actionable insights. [LinkedIn]

Experimental Design Analysis (SAS, SAS/STAT)

• Experimental design Analysis of real state profitability, utilizing CRD, RDB, CRF, RBF methodologies. [GitHub] CO-CURRICULAR ACTIVITIES

• Coached and advised 10+ startup founders at the Volkswagen Technology Event and Startup Weekend in 2020, focusing on data and marketing analysis and customer segmentation to improve product-market fit and scalability.

• managed Volkswagen event operations, leading a team of 40+ on a $50K+ budget.

• Supervised tech startups at four Startup Weekend (2016 to 2020) at the University of Science and Technology and a three Startup Weekend (2017 to 2020) at the Sun Ich company.

• Volunteered to teach algebra, calculus, geometry, statistics, and probability to over 100 students in a charity organization on a regular basis.

CERTIFICATIONS AND ACHIECEMENTS

Awarded Tehran Chess runner-up 2006, demonstrating exceptional problem-solving and analytical ability. Machine learning crash course by Google

Associate Data Analyst in SQL (DataCamp)

SQL Language Certificate (SoloLearn)

Neo4j Graph Data Science Certified (Neo4j)

Associate Data Scientist with Python (DataCamp)

Associate Data Engineering in SQL (DataCamp)

RESEARCH EXPERIENCE

Leveraging Retrieval-Augmented Generation (RAG) for Long-Term Memory in AI Systems Stevens Institute of Technology 2023

• Investigated the application of Retrieval-Augmented Generation (RAG) techniques to enhance long-term memory capabilities in AI systems, focusing on improving contextual understanding and response accuracy.

• Developed and implemented methodologies to integrate RAG with memory-augmented language models, resulting in more efficient data retrieval and processing, and evaluated the impact on overall AI performance.

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