Data Analyst Science

ANAM EJAZ US Citizen

DATA ANALYST

Phone: 914-***-**** Location: NJ Email: ***********@*****.***

SUMMARY

Results-driven Data Analyst with over 4+ years of experience and a robust background in data science and mathematics, leveraging advanced analytical skills to derive actionable insights from complex datasets.

Possesses a unique blend of technical expertise and business acumen, demonstrated through successful projects in the pharmaceutical and financial sectors.

Proficient in a wide array of programming languages, databases, and data visualization tools, including Python, R, SQL, PostgreSQL, Tableau, and Power BI.

Skilled in applying Agile and Waterfall methodologies, managing projects by defining phases, setting milestones, and ensuring timely delivery. Adapts approaches to meet dynamic requirements and maintain project scope, ensuring effective collaboration and successful outcomes.

Proven track record of conducting comprehensive data analysis, developing interactive dashboards, and implementing sophisticated data mining techniques, excelling at translating raw data into strategic business recommendations.

Experienced with Scala for scalable data processing and data engineering tasks, contributing to efficient handling of large datasets and real-time analytics.

Skilled in SaaS platforms, utilizing them for cloud-based data solutions and collaboration, optimizing software deployment, and enhancing accessibility.

Expertise extends to text mining, statistical modeling, and data wrangling, ensuring data integrity and quality throughout the analytical process.

Adept at collaborating with cross-functional teams and presenting complex findings to both technical and non-technical stakeholders, combining strong communication skills with a passion for data-driven decision-making.

TECHNICAL SKILLS

Programming Languages: R, Python, SQL, SCALA, SAAS, and HTML.

Database: MySQL, PostgreSQL, MATLAB, TensorFlow, Oracle.

Data Visualization Tools: Tableau, Power BI, Excel, Looker, Alteryx.

Libraries: NumPy, Pandas, Matplotlib.

Tools: Microsoft Office, Google sheets, Minitab, Powerpoint, Jupiter Notebook.

Version Control Tools: Git, GitHub.

Methodologies: Agile, Waterfall.

Data Analysis Skills: Data Mining, Data Cleansing, Statistical Analysis, Data Visualization, Text Mining,

Data Wrangling, Data Warehousing.

Others: Ability to teach & mentor Decision maker & Problem Solver, Effective communication.

PROFESSIONAL EXPERIENCE

McKesson, NJ Sep 2022 - Current

Data Analyst

Operated on large amounts of data processing drug’s efficacy and safety issues in the PostgreSQL and Oracle environment, built sophisticated queries to identify trends.

Generated data visualizations using Tableau and Excel and shared the results with the stakeholders in form of live updates in existence of drug performance and safety data to support the stakeholders’ informed decisions.

Deployed graphics production with the use of the Matplotlib library in Python, to create custom trends and strategic patterns with regards to drug efficacy and adverse events by presenting the analytical findings in a more vivid and appreciable form in a way that may be understood by both technical and non-technical personnel.

Partnered with healthcare providers to streamline Medicare and Medicaid reimbursement processes. Conducted comprehensive audits and introduced workflow improvements, enhancing efficiency, and optimizing reimbursement rates.

Conducted the data exploration and analysis in Jupiter Notebook, described all steps that were taken to complete each task as well as shared the results with the members of the team with the intention to document all the necessary processes that are essential for repeated analysis of the drug efficacy and safety.

Coordinated with other teams and used sources like GitHub for version control and checking for other team’s code integrity and proper versioning, branches and the pull requests before contributing to work in more than 50 repositories.

Implemented the Waterfall approach in order to organize and control the Drug Efficacy and Safety Analysis project, which aimed at dividing the tasks into the series of phases and establishing strict timelines for the project’s completion and presenting the detailed plans for work.

Performed text mining of qualitative data obtained from electronically stored clinical trials and patient surveys applying text mining NL tools.

Cleaning activities of data involved manipulation through various processes to get them into proper shape for analysis including dealing with missing values, normalization, and encoding of data from continuous and categorical sources.

Prepared regular writings and occasional revues for the decision-making needs, preparing recommendations concerning drugs efficiency and safety to the technical-clinical and strategic clients, preparing more than 25 writings.

Performed data archaeological analysis and data cleansing to improve the quality and accuracy of stored data in accordance with the organizational standards to increase the efficacy of analysis of efficacy and safety of drugs.

BCBS, NJ Apr 2020 – Aug 2022

Data ANALYST

Enhanced data-driven decision making by creating interactive Power BI dashboards, improving KPI comprehension for 50+ executives.

Optimized healthcare program performance through insightful Tableau visualizations, analyzing 3TB of data to inform critical operational decisions.

Improved data quality and consistency by implementing Python-based SQL Server data validation checks, reducing dirty data.

Developed actionable insights from patient data using clustering algorithms, leading to 25% better personalized treatment outcomes.

Created a single source of truth for patient metrics by building SSRS reports connected to a normalized Oracle database, aggregating data from various sources.

Optimized resource allocation by producing 120 insightful capacity planning visuals using Power BI and Tableau, fostering data-driven decision making.

Accelerated data migration by developing a scalable cloud ETL pipeline on AWS (EC2, Lambda), migrating 2TB of legacy healthcare data to Snowflake in under 3 hours.

Reduced healthcare expenses and improved equipment longevity through A/B testing frameworks for medical devices.

Mitigated financial risk by analyzing 1.5 million patient records using SQL, identifying medication inconsistencies, and reducing diagnostic errors.

Leveraged Python's data science ecosystem (NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Plotly, PySpark, and NLTK) to analyze complex financial datasets.

Improved project delivery efficiency by actively participating in agile methodologies, accelerating healthcare project timelines.

EDUCATIONS

Master of Science- Data Science SEP 2021 - MAY 2023

New Jersey Institute of Technology

Master of Science- Mathematics SEP 2009 - MAY 2011

University of Punjab

Bachelor of science – Mathematics, Statistics SEP 2007 – May 2009

University of Punjab

CERTIFICATIONS

Pandas Boot camp 2022: Data Science with Python

Python Developer in 2022: Zero to Mastery

Machine Learning: Hands on in Python and R in Data Science

PROJECTS

Sporting Goods Project (Data Mining and Analysis)

Cleaned and structured unstructured data to identify relevant variables for predictive modeling.

Performed exploratory data analysis to uncover hidden patterns and trends in sales, customer behavior, and product performance.

Implemented data mining techniques to identify key drivers of sales, customer segmentation, and product recommendations.

Restaurant Project (Data Mining and Analysis)

Employed regression and neural network models to forecast sales and customer demand.

Conducted a comparative analysis to evaluate model performance and identify the optimal approach for the given dataset.

Leveraged data-driven insights to optimize pricing, menu offerings, and marketing strategies.

Heart Disease Prediction (Machine Learning by Jupiter Notebook)

Undertook comprehensive data preprocessing including handling missing values, outliers, and feature scaling.

Visualized data distributions and correlations to identify significant predictors of heart disease.

Built and evaluated multiple machine learning models (Logistic Regression, LDA, QDA, Random Forest, and Support Vector Machine) to optimize prediction accuracy.

Implemented hyper parameter tuning to enhance model performance and generalization.

Developed a clear and concise PowerPoint presentation to communicate complex machine learning concepts to a non-technical audience.

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