🎓 MS in Applied Data Science @ The University of Chicago
📍 Chicago, Illinois
📫 sakshi.bokil@gmail.com | LinkedIn
I’m a data-driven problem solver passionate about transforming complex data into actionable insights that drive strategic decisions.
My background combines data analytics, machine learning, and cloud technologies — enabling me to build scalable, intelligent systems that deliver measurable business value.
I’ve applied these skills in professional settings at Deloitte and JLL, and through academic and personal projects that range from predictive modeling to AI-powered automation.
I’m currently seeking Data Scientist and Data Analyst roles where I can apply analytical rigor and creativity to solve impactful business challenges.
Programming: Python (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch), R, SQL, PySpark
Analytics & Visualization: Power BI, Tableau, Excel, Matplotlib, Seaborn
Machine Learning: Supervised/Unsupervised Learning, NLP, Predictive Modeling, Time Series Forecasting
Big Data & Cloud: Google Cloud Platform, AWS, Spark, Hadoop, Docker
Other Skills: Data Wrangling, ETL Pipelines, Feature Engineering, A/B Testing, Model Evaluation, Data Storytelling
Data Analyst Intern (Summer 2025)
- Automated Risk Control Test Matrix generation using an agent-based data pipeline, reducing manual audit effort by 40%.
- Built Power BI dashboards to visualize cross-functional risk trends, supporting data-driven decision-making for senior stakeholders.
- Analyzed large-scale audit datasets on Databricks (SQL + Python) to uncover recurring risk patterns.
Associate Analyst (2023–2024)
- Conducted and analyzed 100+ accessibility defects, delivering insights that led to measurable UI/UX improvements.
- Led client interviews and synthesized findings into a data-backed accessibility scorecard for executives.
- Designed interactive Tableau dashboards to visualize market insights, saving 15+ hours of manual reporting each month.
- Conducted quantitative and qualitative analysis across 100+ industry reports to identify emerging business opportunities.
- Designed a LangChain-based routing agent to classify user intents for HR relocation queries with >90% accuracy.
- Built a conversational UI in Chainlit, integrating secure file uploads and dynamic data collection for process automation.
- Developed a multi-label classification model to tag job descriptions by required skills.
- Created a bi-directional recommendation system to match resumes with ideal job postings using Streamlit.
- Trained a CNN (MobileNetV2) for real-time American Sign Language (ASL) gesture recognition.
- Integrated semantic analysis to provide contextual translation and emotional tone scoring.
- Built a Random Forest model to predict song popularity using artist and audio features.
- Introduced genre-based clustering to improve interpretability and support marketing strategies.
- Processed 30GB of text data using SparkNLP on GCP to identify developer sentiment and topic trends.
- Trained logistic regression models for post classification with SparkML to enhance tagging accuracy.
This repository and its contents are shared for educational and professional demonstration purposes only.
Feel free to explore or connect to discuss collaborations in data science, analytics, and AI.