Data Analyst Machine Learning

Vineel Reddy Salla

Data Analyst

+1-508-***-**** *********@*****.*** Boston, MA

SUMMARY

• Demonstrated proficiency as a data analyst with 3+ years of experience, employing a versatile skill set across various methodologies and programming languages.

• Experience using Agile and Waterfall methodologies to drive end-to-end data analysis projects, ensuring efficient project delivery.

• Skilled in utilizing Python and R packages for advanced data manipulation, visualization, and statistical analysis.

• Experienced in utilizing visualization tools to create impactful visualizations and reports for data-driven decision-making.

• Proficient in working with various databases, including MySQL, NoSQL, etc., ensuring data accessibility and integrity. SKILLS

Methodologies: SDLC, Agile, Waterfall

Programming Language: Python, R, SQL, SAS

IDEs: Visual Studio Code, PyCharm, Juypter Notebook Packages: NumPy, Pandas, Matplotlib, Seaborn, SciPy, ggplot2, Scikit-learn Visualization Tools: Tableau, Power BI, Microsoft Excel Cloud Technologies: AWS, Azure, GCP, DataBricks

Database: MySQL, SQL Server, PostgreSQL, NoSQL

Version Control Tools: Git, GitHub, GitLab

Other Skills: Data Cleaning, Data Wrangling, Critical Thinking, Communication Skills, Presentation Skills, Problem-Solving, Data Analytics, Decision-making, Data Architecture, Data Management Operating System: Windows, Linux, Mac

EXPERIENCE

Honeywell, USA Sep 2023 – Present

Data Analyst

• Led the development of predictive maintenance models for production equipment by analyzing sensor data. Applied advanced statistical and machine learning techniques to forecast potential breakdowns, reducing equipment downtime by 15%.

• Engineered efficient data pipelines using Python libraries like Pandas and NumPy to process and clean large-scale sensor data, optimizing workflows and reducing data processing time.

• Developed interactive Tableau dashboards to visualize sensor data trends and equipment health, enabling real-time monitoring for maintenance teams and supporting proactive decision-making.

• Utilized AWS cloud solutions to store and process high volumes of sensor data, ensuring scalable and reliable data access for analytics, leading to a 30% improvement in data retrieval efficiency.

• Designed and optimized MySQL databases for sensor data storage, creating efficient data access methods for predictive modeling and enhancing data accessibility for various teams across the organization.

• Developed and deployed machine learning models (LSTM RNN and Isolation Forest) for predicting the likelihood of equipment failure based on sensor data, improving the accuracy of maintenance scheduling and significantly extending equipment life span.

• Created custom visualizations in Python (Matplotlib and Seaborn) to detect anomalies in sensor data, allowing maintenance teams to identify irregular patterns in equipment behavior and respond to potential issues promptly. Mindtree, India Jan 2020 – Jul 2022

Data Analyst

• Analyzed historical sales data to forecast inventory needs, applying time-series analysis and regression models to predict stock demand accurately, reducing stockouts by 20% and overstock situations by 15%.

• Built Power BI dashboards to track sales performance across regions and products, providing management with actionable insights into inventory trends and improving stock allocation efficiency.

• Implemented data processing pipelines in Azure Databricks to handle large-scale sales data, enhancing data workflows and reducing data processing latency.

• Utilized PostgreSQL to execute complex queries for extracting, transforming, and aggregating sales data, enabling accurate inventory tracking and supporting replenishment decisions.

• Integrated Python and SAS for comprehensive data analysis, developing models to predict sales patterns and inventory requirements, which improved the accuracy of demand forecasting by 18%.

• Developed what-if analysis models to simulate various sales scenarios and optimize inventory levels, ensuring stock availability during peak seasons and reducing excess inventory costs by 15%.

• Integrated multiple data sources, including CRM systems, supply chain databases, and sales transactions, to create a unified view for analysis and reporting, enhancing the accuracy of sales forecasts and inventory management across all business units. EDUCATION

Masters in Computer Science May 2024

Arizona State University, Tempe, AZ

Bachelors in Computer Science Jun 2022

SRM Institute of Science & Technology, Chennai, India

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