Data science intern

PROFESSIONAL SUMMARY

Graduate student with fundamental knowledge in data analytics who is passionate about generating ideas, collaborating, and creating. I firmly believe in authenticity and sincerity while working on any project and enjoy dynamic teamwork and use my soft skills to enhance my training and professional experience. www.linkedin.com/in/saisruthi-kotagiri +1-313-***-**** ad63e0@r.postjobfree.com SAISRUTHI KOTAGIRI

EDUCATION

Wayne State University – College of Engineering, Detroit, MI Masters in Data science and Business analytics

GPA: 3.95

Expected Graduation: Aug 2024

National Institute of Technology – Surat, India

Bachelor of Technology in Mechanical Engineering GPA: 3.57 TECHNICAL & COMPUTER SKILLS

Programming Language: Python, HTML, SQL, R.

Skills: AutoCAD, SolidWorks, Tableau, Excel, Statistics, Power BI, Azure DevOps, Databricks,Omniverse. Database: MySQL.

OS: Windows and Mac.

Libraries/Frameworks: Pandas, TensorFlow, NumPy, Matplotlib, PySpark,MLflow,OpenCV,YOLO v8. Cloud Platforms: Azure Cloud Computing Platforms (Azure ML Studio, Azure Data Lakes, ADX), Google Cloud Platform (GCP).

WORK EXPERIENCE

Data Science Intern - Borgwarner, MI [Jan 2024 - Current] Digital Twin Project

As a Data Science Intern, I imported electric vehicle telematic data from Azure Data Lake, focusing on Parquet files containing vehicle telemetry data.

I engineered solutions to address varying time frequencies in data, merging diverse datasets based on startup sequences to create a comprehensive view of each session. I developed a pipeline for anomaly detection across driving and charging sessions and created a trip summary feature to aggregate key trip information for real- time monitoring and decision-making using a Grafana dashboard through ADX. Additionally, I contributed to the construction of a time series modeling pipeline for predicting the State of Health (SOH) of electric vehicles, enhancing proactive maintenance strategies. Requirement Extraction Project

As a Data Science Intern, I initially developed a neural network model that achieved an 85% F1 score and approximately 90% accuracy in categorizing client requirement documents. Utilizing Azure Databricks and Azure DevOps, I streamlined model development and project management. During this process, I implemented prompt engineering techniques and developed a multilingual machine learning model using OpenAI embedding to identify system requirements from a broader set of client submissions, aligning with company-defined standards. I was also involved in learning LLM prompt engineering and comparing its output with the neural network model. 4417 2nd Avenue, Detriot, 48201, MI, USA

ACHIEVEMENTS AND CERTIFICATION

Data Science with Python [2022]

Machine Learning [2022]

Deep Learning with Keras and Tensorflow [2022]

Dassault Systems [2020]

Associate-Sustainability (CSWA-SD) on July 21 2020 Academic exam at Engineering Technique – EDU, Aggregate: 29.0 / 30.0 [2020] Mercedes-Benz Greener Manufacturing- To Reduce the time a Mercedes-Benz spends on the test bench. [Novemeber 2021]

Developed ML models to predict Mercedes component performance enabling reduced physical testing through accurate simulated testing.

Analyzed sensor data patterns and optimized neural network and SVM algorithms for reliable performance forecasting.

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Comparative Analysis of Country Metrics via Tableau [December2022] Engineered an interactive dashboard integrating multiple datasets for a comparative country analysis, with dynamic URL actions linking to World Bank insights. Introduced user-driven parameters, visual growth indicators, and trend illustrations, optimizing for user experience and data clarity.

PROJECTS

Retail analysis with Walmart data- Built prediction models to forecast demand and selected the model which gives the best accuracy. [October 2021]

Built prediction models to forecast retail demand by collecting data including sales, promotions, weather and training linear regression, decision tree and random forest models. Evaluated each model's performance and selected the one with highest accuracy, using it to generate accurate demand forecasts.

EV Charging Station Locator App [Dec 2023]

Created a Flask-based app on GCP to locate nearest EV charging stations, utilizing Python for backend logic and OSRM for routing.

Integrated with OSRM API for real-time travel distances and times, enhancing EV users' convenience by recommending the nearest charging stations.

Tackled EV users' range anxiety by facilitating easy access to charging stations, supporting sustainable transportation growth in the United States.

MS DSBA Practicum Project - Automated Disassembly Process Planning [May 2024 - Present] I partnered in a groundbreaking project initiated by IndustryX.org, aiming to transform manufacturing processes with automated disassembly, leveraging AI and vision technologies. Using the Omniverse platform, I implemented AI vision automation to be deployed in industrial settings. I utilized the YOLO v8 library to develop robust machine learning models that accurately identify and pinpoint various fasteners, significantly improving the efficiency and precision of automated disassembly systems. I crafted sophisticated models for robotic part handling, ensuring robust grip and precise manipulation, critical for efficient disassembly operations.

Additionally, I implemented cutting-edge vision-based scanning methods for meticulous part inspection and formulated algorithms to generate optimized disassembly process plans. Research Assistant - Education Counseling [July-Dec 2023] Engaged in a project on Twitter Data Analysis and topic modelling. Focused on data mining from Twitter, encompassing data collection, processing, and analysis to extract valuable insights for the research objectives.

Used BERT model to find out the different topics which are discussed in diffrent tweets. AL RAHA Mechanical Equipment Company WLL- Abu Dhabi (Internship) [July 2021] Acquired practical expertise in upholding safety and quality benchmarks for the company's offerings. Proficiently conducted inspections, tests, and data assessments to verify adherence to quality protocols and industry regulations.

Enhanced skills in employing tools like hazard recognition, risk evaluation, and incident inquiry for process improvement.

While the neural network model performed well, the prompt engineering techniques yielded better results. Consequently, our team decided to proceed with the prompt engineering approach. Additionally, I contributed to the ongoing development of a Retrieval-Augmented Generation (RAG) system for improved requirement extraction and classification, integrating cutting-edge AI and machine learning technologies.

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