I’m Sofiya Sultana, a data analyst with an M.Sc. in Data Science and hands-on experience building end-to-end SQL/Python pipelines, automated reporting routines, and interactive dashboards in Power BI and Streamlit. I enjoy turning complex datasets from financial, sales, and operational domains into clear KPI tracking and decision-ready insights using advanced SQL (CTEs, window functions like LAG and DENSE_RANK) and reliable data validation practices. In my recent internships, I worked on deep learning for automated image classification and interpretability, and I also developed an AI/ML workflow to automate research paper ingestion and summarization. I follow a STAR approach to translate raw data into actionable business outcomes—strengthening data quality, improving accessibility for stakeholders, and delivering executive-ready metrics and visual storytelling.

Sofiya Sultana

I’m Sofiya Sultana, a data analyst with an M.Sc. in Data Science and hands-on experience building end-to-end SQL/Python pipelines, automated reporting routines, and interactive dashboards in Power BI and Streamlit. I enjoy turning complex datasets from financial, sales, and operational domains into clear KPI tracking and decision-ready insights using advanced SQL (CTEs, window functions like LAG and DENSE_RANK) and reliable data validation practices. In my recent internships, I worked on deep learning for automated image classification and interpretability, and I also developed an AI/ML workflow to automate research paper ingestion and summarization. I follow a STAR approach to translate raw data into actionable business outcomes—strengthening data quality, improving accessibility for stakeholders, and delivering executive-ready metrics and visual storytelling.

Available to hire

I’m Sofiya Sultana, a data analyst with an M.Sc. in Data Science and hands-on experience building end-to-end SQL/Python pipelines, automated reporting routines, and interactive dashboards in Power BI and Streamlit. I enjoy turning complex datasets from financial, sales, and operational domains into clear KPI tracking and decision-ready insights using advanced SQL (CTEs, window functions like LAG and DENSE_RANK) and reliable data validation practices.

In my recent internships, I worked on deep learning for automated image classification and interpretability, and I also developed an AI/ML workflow to automate research paper ingestion and summarization. I follow a STAR approach to translate raw data into actionable business outcomes—strengthening data quality, improving accessibility for stakeholders, and delivering executive-ready metrics and visual storytelling.

See more

Experience Level

Expert
Expert
Expert
Expert
Expert
Intermediate
Beginner
Beginner
See more

Language

English
Advanced
Hindi
Advanced
Telugu
Intermediate
Urdu
Intermediate

Work Experience

Virtual AI/ML Intern at Infosys Springboard Virtual Internship
November 1, 2025 - January 1, 2026
Designed an end-to-end NLP pipeline to automate literature review and paper synthesis by fetching research metadata, extracting content from PDFs, and generating concise summaries using Python. Implemented fault-tolerant ingestion with Semantic Scholar API, PyMuPDF, and Tesseract OCR, including fallback logic. Applied TF-IDF sentence ranking with NLTK/scikit-learn to generate structured summaries and outputs to unified CSV metadata. Delivered a functioning automated retrieval and summarization flow that reduced document research time.
Machine Learning Intern at Mahindra University, Hyderabad, India
May 1, 2025 - July 1, 2025
Developed a CNN-based image classification and trend analysis system for operational fashion datasets using TensorFlow/Keras. Built Python preprocessing workflows to normalize and reshape image data and used stratified sampling to reduce manual preparation time. Integrated Grad-CAM for explainable AI to analyze feature maps and performed cluster-based error analysis to isolate misclassification patterns across multi-class categories. Produced executive reporting with Matplotlib/Seaborn dashboards and summarized model performance metrics to support decision-making.

Education

B.Sc at Bhavan’s Vivekananda Degree College, Secunderabad
January 1, 2021 - December 31, 2024
Intermediate at Bhavan’s Sri Aurobindo Junior College, Secunderabad
January 1, 2019 - December 31, 2021
SSC at Valerian Grammar High School, Secunderabad
January 1, 2019 - December 31, 2019
B.Sc at Bhavan’s Vivekananda Degree College, Secunderabad
January 1, 2021 - December 31, 2024
Intermediate at Bhavan’s Sri Aurobindo Junior College, Secunderabad
January 1, 2019 - December 31, 2021
SSC at Valerian Grammar High School, Secunderabad
January 1, 2019 - December 31, 2019
M.Sc. in Data Science at GITAM University
January 1, 2024 - January 1, 2026
B.Sc. in Computer Science at Bhavans Vivekananda Degree College
January 1, 2021 - January 1, 2024

Qualifications

Generative AI & LLM Workshop
January 1, 2024 - December 31, 2024
Introduction to Computers & Office Productivity Software – Coursera
January 1, 2024 - December 31, 2024
Google Data Analyst – Coursera
January 11, 2030 - June 20, 2025
Microsoft Data Visualization - Coursera
January 11, 2030 - June 20, 2025
Hands-on Circuit Design, Testing & Troubleshooting Workshop
January 1, 2024 - December 31, 2024
MATLAB Onramp Training Course – MathWorks
January 1, 2024 - December 31, 2024
Generative AI & LLM Workshop
January 1, 2024 - December 31, 2024
Introduction to Computers & Office Productivity Software – Coursera
January 1, 2024 - December 31, 2024
Google Data Analyst – Coursera
January 11, 2030 - June 20, 2025
Microsoft Data Visualization - Coursera
January 11, 2030 - June 20, 2025
Hands-on Circuit Design, Testing & Troubleshooting Workshop
January 1, 2024 - December 31, 2024
MATLAB Onramp Training Course – MathWorks
January 1, 2024 - December 31, 2024

Industry Experience

Software & Internet, Professional Services, Education, Computers & Electronics, Financial Services
    Receipto AI: End-to-End Expense Automation & RAG Assistant
    Receipto AI is an end-to-end financial automation system and interactive dashboard. It leverages Google's gemini-3.6-flash model via structured Pydantic schemas to extract precise JSON data from messy receipts, automatically indexing the structured records across a dual-database architecture for seamless retrieval-augmented generation (RAG) and financial querying. 🚀 Key Features Structured LLM Parsing: Parses unstructured receipt text into validated JSON schema fields (merchant, date, total_amount, category, tax_amount) using gemini-3.6-flash and Pydantic. Dual-Database Pipeline: * Relational Storage (SQLite): In-memory structured SQL database (expenses table) for fast tabular storage and numerical aggregations. * Vector Storage (ChromaDB): Generates semantic summaries and indexes them in a vector collection (expense_vectors) for similarity search. RAG-Powered Financial QA: Performs semantic vector search over stored expense summaries to synthesize concise natural-language answers to spending queries. Interactive Frontend Options: Includes both a Python Gradio web interface and an ultra-modern React + Tailwind CSS dashboard template.