Data Analyst Machine Learning

Naila Jan

**********@*****.*** LinkedIn +1-703-***-****

SUMMARY

Results-driven Data Analyst with 5+ years of experience in transforming complex data into actionable insights. Expertise in data analytics, machine learning, and data visualization, with a strong background in structured and unstructured data analysis. Proficient in Python, R, SQL, Power BI, and Tableau, with hands-on experience in NoSQL databases like MongoDB. I am skilled in automating workflows, building predictive models, and optimizing data processes to drive strategic decision-making. Adept at leveraging statistical analysis, A/B testing, and NLP techniques to solve business problems. Passionate about exploring new technologies and delivering innovative solutions in a data-driven environment. TECHNICAL SKILLS

Language (Python, R, SQL, HTML, JavaScript), Statistics (Excel-based Analysis, T-tests, A/B testing, Chi-square Testing, Significance tests), Machine Learning( Regression, Classification, clustering, Random Forests, Decision Trees), Tools & Software (Jupyter notebook, R-Studio, Dataiku, Confluence, Jira/JAR, MS Project, MS Power Automation) Databases (MongoDB, MySQL) Data Visualization & Reporting (Power-BI, Tableau)

EXPERIENCE

Data Analyst

Vanguard, Malvern, PA Feb 2023 – Present

• Automated daily data extraction from ServiceNow using Microsoft Power Automate and Python, reducing manual effort by 1 hour/day and ensuring timely data delivery.

• Spearheaded a project to automate plane shift tracking, reducing behind-plane instances by 50% and optimizing resource allocation through cost analysis.

• Migrated Excel-based reporting to Power BI, replacing macros with automated workflows, enabling real-time progress tracking and daily KPI monitoring.

• Developed a unified KPI dashboard in Power BI, consolidating multiple metrics into a single report, eliminating redundant dashboards, and automating monthly reporting.

• Acted as Product Owner for internal databases, managing data updates, stakeholder access, and vendor collaboration to ensure system efficiency.

Computer Science Lecturer

Abdul Wali Khan University Mardan (AWKUM), PK Feb 2019 – July 2021

• Delivered lectures on database management, programming, and data analysis to 150+ undergraduate and graduate students.

• Designed course materials and assessments, integrating real-world data analysis scenarios to enhance learning outcomes.

• Mentored students in applying programming and data analysis concepts to practical problems, fostering analytical thinking and problem-solving skills.

Research Assistant (Data Analyst)

National University of Computers and Emerging Sciences Jan 2017 – Feb 2019

• Conducted data collection, cleaning, and transformation for IoT-based home/office automation projects, improving data accuracy and research validity.

• Performed statistical analysis, hypothesis testing, and A/B testing using R-Studio and Python, contributing to high-impact publications in IEEE.

• Published research papers on software quality assurance, requirements engineering, and testing methodologies. Naila Jan

**********@*****.*** LinkedIn +1-703-***-**** PROJECTS AND COMPETITIONS

Model Analysis with ML & SQL— HULT, San Francisco, California March 2022 - May 2022

• Conducted data preprocessing (cleaning, normalization, feature engineering) using Python (Pandas, NumPy) and MySQL.

• Built and deployed machine learning models (logistic regression, decision trees, k-means clustering) to predict wholesaler and client behavior, achieving 82% accuracy.

• Automated financial reporting using SQL stored procedures, reducing manual effort by 50%.

• Created interactive Power BI dashboards to visualize sales trends and customer segmentation, enabling data-driven decision-making.

Sentimental Analysis using NLP on Airbnb Data — HULT, San Francisco, California Jan 2022 - Apr 2022

• Performed text preprocessing (tokenization, stemming, lemmatization) on unstructured Airbnb review data using R-Studio.

• Applied NLP techniques (sentiment analysis, topic modeling) to identify customer sentiment trends and key themes.

• Built a sentiment classification model (Naive Bayes, SVM) with an F1 score of 78%.

• Designed Tableau dashboards to visualize sentiment trends and geographic insights, leading to a 60% increase in customer satisfaction.

EDUCATION

Master of Science in Business Analytics Sep 2021 – Aug 2022 HULT International Business School— San Francisco, California MS, Software Engineering Aug 2016 - Aug 2018

National University of Computers and Emerging Science (FAST) PUBLICATIONS

• https://ieeexplore.ieee.org/abstract/document/8728904

• https://ieeexplore.ieee.org/abstract/document/8945656

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