RAHUL REDDY TADURI

ad4s2r@r.postjobfree.com +1-857-***-**** LinkedIn GitHub

SUMMARY

Experienced data professional with over 3+years of expertise in impactful data modeling and analysis, significantly contributing to crucial decision-making processes and continuous learning.

Coordinated within a 3-member analyst team, contributing to ideation and implementation, resulting in a 35% improvement in data flow efficiency.

Utilized SQL queries for data extraction from Hadoop-based databases in Hive, working on large-scale datasets, supporting streamlined communication strategies and improving inter-team collaboration by 20%.

Applied machine learning algorithms such as linear regression and random forest, actively engaging in data governance to reduce data quality issues by 25%.

Crafted tailored Power BI dashboards, leading to a 25% increase in engagement.

Collaborated with cross-functional teams and crafted comprehensive reports with detailed narratives and user-friendly visualizations.

Led a predictive analytics project in the resale market, achieving 70% and 74% accuracies with linear regression and gradient boost models, applying ARIMA for precise forecasting, and delivering insightful Power BI dashboards.

Spearheaded an image recognition initiative using deep learning, attaining an 80% predicted training accuracy with a pre-trained VGG16 model and TensorFlow, effectively gauging the likelihood of images containing dogs with a 75% predicted accuracy.

Implemented an advanced artificial neural network model for root cause analysis in IT operations, achieving a remarkable 95.4% predictive accuracy using categorical cross-entropy and strategic representation of target variables.

Successfully transformed real-time unstructured alarm data into a refined dataset, applying natural language processing and clustering techniques, resulting in the identification of 30 distinct groups for efficient false alarm detection and grouping.

Proficient in Excel, Python, and R enabling comprehensive data analysis and visualization, resulting in increased efficiency and informed decision-making.

Demonstrated expertise in statistical analysis, programming, and the successful implementation of predictive maintenance programs, leading to significant cost savings and a notable reduction in downtime.

Developed and implemented KPI’s based on data insights, facilitating real-time monitoring and assessment of project milestones, while also managing operational research techniques for transportation and inventory solutions, including vehicle routing, supply chain optimization, and inventory control.

PROFESSIONAL EXPERIENCE

Paricharya Consulting Private Limited, Hyderabad India July 2020 – May 2022

Data Analyst

Coordinating within a 3-member analyst team in a dynamic retail data environment, I contributed to the ideation and implementation of various business functions. I developed a robust data cleaning and preprocessing pipeline using SQL and Python, significantly reducing discrepancies by 30%. Additionally, I applied Principal Component Analysis (PCA) to identify key features and reduce dimensionality in large datasets, improving computational efficiency and enhancing model performance.

To facilitate informed decision-making within the retail sector, I created compelling visualizations tailored to specific business needs. Crafted Power BI dashboards led to a notable 25% increase in stakeholder engagement. Additionally, I played a pivotal role in Python-based exploratory data analysis, enhancing data accuracy by 15%. Furthermore, I utilized decomposition analysis to extract trend and seasonal components from historical sales data, providing valuable insights into seasonal trends and long-term sales patterns.

Utilizing SQL queries for data extraction from a Hadoop-based database in Hive, I supported the implementation of streamlined communication strategies within the retail framework. This initiative resulted in a marked 20% improvement in inter-team collaboration and project efficiency. Moreover, I employed decomposition analysis techniques to decompose complex time series data, aiding in the identification of underlying patterns and trends.

Predicting future trends crucial for the retail industry through comprehensive time series analysis and forecasting techniques, I applied various statistical methods to enhance strategic decision-making. This includes but is not limited to:

1.Seasonal decomposition: Identifying seasonal patterns in retail sales data.

2.Trend analysis: Identifying long-term trends in sales or customer behavior.

3.Exponential smoothing: Smoothing techniques to remove noise and identify underlying trends.

4.Autoregressive Integrated Moving Average (ARIMA): Modeling and forecasting time series data.

Employing machine learning algorithms, notably linear regression and random forest, I actively engaged in data governance, thereby reducing data quality issues by 25%. In Random Forest, I optimized model parameters and feature selection techniques to improve prediction accuracy by 12%. Additionally, in Linear Regression, I conducted feature engineering and regularization techniques to enhance model interpretability and performance, resulting in a 10% increase in accuracy.

Collaborating with cross-functional teams within the retail sector, I leveraged advanced analytics resulting in a substantial 35% improvement in data flow efficiency. By integrating diverse datasets and employing sophisticated analytical methodologies, I facilitated a seamless flow of information critical for decision-making processes.

Crafted comprehensive reports tailored to retail stakeholders, incorporating detailed narratives and user-friendly visualizations. This ensured that stakeholders comprehended the end-to-end data analysis process, fostering informed decision-making and collaboration within the dynamic retail landscape.

SKILLS : Python, SQL, Hadoop, Hive, Power BI, Excel, time series and forecasting(ARIMA), ML algorithms(linear regression & Random Forest).

Everest Organics Limited, Sadashivpet, Sangareddy India May 2019 – June 2020

Jr Project Analyst

Leveraged data visualization in excel to present complex technical information in a clear and concise manner, facilitating informed decision-making by stakeholders.

Implemented a predictive maintenance program based on historical data analysis, reducing downtime by 15% and contributing to significant cost savings.

Utilized programming languages such as Python / MATLAB to automate data processing tasks, showcasing proficiency in data manipulation and programming for efficiency.

Developed key performance indicators (KPIs) based on data insights, allowing for real-time monitoring and assessment of project milestones, enhancing project management effectiveness.

Applied statistical methods to analyze product performance data, leading to the identification of design enhancements that increased product reliability by 20%, underscoring a data-driven approach to problem-solving.

Managed Operational Research techniques for transportation and inventory solutions like vehicle routing, supply chain, Inventory control.

SKILLS : Excel, Python, Matlab, KPI’s, Autocad, Predictive maintenance, Performance testing, operational research.

PROJECTS

Predective Analytics in Resale market Power Bi, Regression Analysis, Gradient Boost, ARIMA, MYSQL January 2024 – March 2024

Extracted the data from the Snowflake cloud based data warehousing playform usig SQL queries.

Analyzed and processed a sneaker resale market dataset comprising six million records.

Executed feature engineering and implemented one-hot encoding for precise predictive modeling.

Fitted, trained, validated, and tested linear regression and gradient boost models, achieving impressive accuracies of 70% and 74%, respectively.

Applied ARIMA on the target variable as sold price, forecasting its values with precision.

Executed a comprehensive dashboard analysis, comparing pre- and post-forecast values of the target variable.

Image recognition using Deep Learning Image Recognition, Deep Learning, CNN October 2023 – December 2023

Loaded a pre-trained VGG16 model using TensorFlow and essential libraries.

Extracted features from images in "dog" and "not_dog" folders, preprocessed, normalized, and stored arrays and labels.

Developed and trained a neural network with TensorFlow, Sequential, Dense, Dropout, Flatten, Path, and joblib, preserving the model structure to JSON and weights to H5 files, attaining a predicted training accuracy of about 80%.

Loaded the trained model structure and weights, prepared a test image, extracted features using VGG16, made predictions, and gauged the likelihood of the image containing a dog, achieving a predicted accuracy of approximately 75%.

Root Cause Analysis for IT Operations Using ANN Model Python, NLP, Deep Learning July 2023 - September 2023

Implemented Keras 2.0 for advanced artificial neural network modeling in deep learning.

Strategically represented the target variable, combining label and one-hot encoding.

Orchestrated a balanced 90:10 data split for robust model training.

Configured hyperparameters like epochs and batch size for a Sequential model with softmax activation.

Attained a notable 95.4% predictive accuracy using categorical cross-entropy.

False Alarm Detection and Grouping Python, NLP, Classification Analysis, Clustering March 2023 – July 2023

Manipulated six-month real-time unstructured alarm data into a refined dataset (3 Million x 9) for a specific one-month timeframe.

Conducted thorough exploratory data analysis, natural language processing, and feature engineering.

Employed the WCSS metric in the elbow method to pinpoint the optimal number of clusters and executed K-fold cluster analysis.

Applied label encoding for efficient grouping, resulting in the identification of 30 distinct groups.

EXTRACURRICULAR

Awarded the 1st prize for a poster presentation on the topic "Chat bot Model: Text Analytics and Summarization" at the Faculty Development Summit at Northeastern University on October 27, 2023.

EDUCATION

Northeastern University, Boston MA USA September 2022 - March 2024

Masters of Professional studies in Data Analytics, (Concentration: Applied Machine Intelligence)

Jawaharlal Nehru University, Hyderabad TS INDIA August 2015- May 2019

Bachelor Of Technology in Mechanical Engineering

RELEVANT COURSEWORK

Artificial intelligence & Machine Learning

Data Mining

Data Base Management

Data warehouse & SQL

Data Analytics

Communications & Visualizations

Big Data

Business Intelligence

TECHNICAL SKILLS

Programming: Python(Jupyter notebook), R, SQL, C, Excel

Data Engineering & Databases: ETL workflows, Airflow, Hadoop, Hive, Cloud era, Apache Spark, DB Browser (SQ Lite), Google Big Query, MySQL, MongoDB.

Tools & Technologies: Azure, Google Analytics 4, Power BI, Tableau, Docker, Git, Excel, OpenCV, Agile development methods(kanban, six sigma), software development life cycle(SDLC), fine tuning, A/B testing and hypothesis testing, anomaly detection.

Modeling: Machine Learning, statistical modeling, Deep Learning (Convolutional & Recurrent Neural Networks), NLP, Large Language Modeling.

Frameworks: Pandas, Numpy, Scikit-Learn, Tensor Flow, Py Torch, PySpark, Scipy, Matplotlib, Sea born, Keras 2.0, Tidyverse.

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