Data Analyst Machine Learning

SUMMARY

Anu Nithya Patchika DATA ANALYST

New Jersey, USA 1+ 551-***-**** ****************@*****.***

Data Analyst with 2.5 years of experience in leveraging advanced data analytics and machine learning techniques to derive actionable insights and drive strategic decision-making. Proficient in Python, SQL, and data visualization tools such as Power BI and Tableau. Experienced in designing predictive models, conducting complex data analysis, and developing interactive dashboards. Skilled in data governance, ETL processes, and cloud technologies, ensuring data integrity and security while optimizing data workflows and reporting efficiency.

SKILLS

Methodologies: SDLC, Agile, Waterfall

Languages: Python, SQL, R

IDEs: Visual Studio Code, PyCharm, Jupyter Notebook

Packages: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn, dplyr, ggplot2, Keras

Visualization Tools: Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP)

Cloud Technology: Amazon Web Services (AWS)

Database: MongoDB, Snowflake, MySQL, SQL Server, PostgreSQL

Other Skills: SSIS, SSRS, SAP, ETL Tools, Machine Learning Algorithms, Probability distributions, Confidence Intervals, ANOVA, Hypothesis Testing, Regression Analysis, Linear Algebra, Advance Analytics, Data Mining, Data Visualization, Data warehousing, Data transformation, Data Storytelling, Association rules, Clustering, Classification, Regression, A/B Testing, Forecasting & Modelling, Data Cleaning, Data Wrangling, Jira, Git, GitHub, Windows, Linux, Mac OS

EDUCATION

Master of Science in Data Science –, New Jersey, USA

Bachelor of Technology in Computer Science and Engineering - Mahatma Gandhi Institute of Technology, India

WORK EXPERIENCE

Data Analyst Northwell Health, NY Jan 2024 – Present.

Analyzed and processed extensive datasets from multiple healthcare systems, implementing advanced data preprocessing techniques to optimize patient flow efficiency by 30%, leading to reduced wait times and enhanced patient satisfaction.

Developed and deployed machine learning models using Python and Scikit-Learn to accurately predict patient admissions, achieving a 25% increase in forecast accuracy and facilitating improved resource allocation.

Utilized AWS S3 for secure and scalable data storage solutions, and leveraged AWS Redshift for high-performance data warehousing, enabling rapid retrieval and analysis of large-scale healthcare data.

Designed and developed dynamic Power BI dashboards for interactive reporting, empowering stakeholders to monitor patient admissions and operational performance, leading to a 35% increase in data-driven decision-making.

Utilized Mulesoft’s Anypoint Studio to design, develop, and deploy APIs for seamless data exchange between systems.

Developed and implemented API-based solutions using Mulesoft Anypoint Platform to integrate diverse data sources.

Conducted complex SQL queries to perform ETL (Extract, Transform, Load) operations from multiple healthcare databases, optimizing query performance by 35% using indexing, partitioning, and query tuning techniques.

Ensured compliance with HIPAA regulations by implementing robust data governance practices, including encryption and data anonymization protocols, maintaining high standards of data security and patient confidentiality.

Created detailed data visualizations using Matplotlib and Seaborn for Exploratory Data Analysis (EDA) within Jupyter Notebooks, uncovering critical insights to guide strategic decision-making and operational improvements.

Implemented advanced DAX functions in Power BI to create custom measures and Key Performance Indicators (KPIs), improving the accuracy of forecasting models and enhancing resource allocation strategies.

Used Mulesoft Anypoint Monitoring to track and analyze data flow performance, ensuring optimal operation and timely data delivery.

Developed comprehensive functional mappings and System-to-Target (S2T) documentation, enhancing communication and collaboration between technical and non-technical stakeholders.

Data Analyst Softage Group, India Jul 2022 – Jul 2023

Developed comprehensive risk assessment reports using advanced predictive modeling techniques, directly contributing to a 15% enhancement in loss prevention and a 20% improvement in early fraud detection rates.

Developed reusable data flows in Mulesoft to automate business processes and reduce manual data manipulation.

Performed complex data analysis leveraging SQL for data extraction and transformation, combined with Python libraries (Pandas, NumPy, Scikit- learn) for statistical analysis and predictive modeling, leading to increased accuracy in fraud pattern identification.

Created and deployed interactive Tableau dashboards with real-time data integration to visualize transaction patterns, detect anomalies, and monitor key performance indicators, enabling stakeholders to make informed decisions swiftly.

Created and automated ETL processes using Mulesoft to extract, transform, and load data from various sources into a central data warehouse, optimizing business reporting.

Analyzed transactional data for over 1 million accounts using advanced machine learning algorithms, resulting in a 25% reduction in fraudulent activities and cost savings of approximately $3 million annually.

Automated data workflows using Mulesoft’s Anypoint Studio to reduce manual data entry and increase operational efficiency.

Utilized AWS S3 for secure, scalable data storage solutions and implemented AWS Glue for ETL processes, streamlining data workflows and reducing processing time by 40%.

Applied rigorous A/B testing methodologies to evaluate fraud detection strategies, leading to the deployment of an enhanced algorithm that boosted fraud detection rates by 35%.

Ensured compliance with financial regulations, including GDPR, CCPA, and Basel III, by establishing robust data governance frameworks and conducting regular audits, safeguarding the organization's reputation and mitigating regulatory risks.

Developed advanced ad-hoc reports and financial models to support executive decision-making, incorporating scenario analysis and stress testing for strategic planning and risk assessment under volatile market conditions.

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