Machine Learning Data Analyst

u Anusha Reddy Mudamala

Dallas, TX ******.******@*****.*** Ph no: 469-***-**** LinkedIn

Professional Summary

Data professional adept in implementing technical systems, developing user-accepted tools, and driving process improvement through predictive modeling and machine learning. Excels in data mining, inventory forecasting, and optimizing operations, substantiated by significant cost savings and efficiency enhancements in past roles

Work History

Data Engineer Intern Nov 2022-March 2023

Walmart (contract), Dallas, Texas

• Implemented Auto purge in Data Lakes using Scala, GCS as per new CCPA rules

• Migrated 600 Automic jobs to Airflow while ensuring data quality and sanity.

• Engaged in development of UAT mapping tool, implementing the deployment of the tool.

• Created Confluence documents, requirements documents, and core solution documentation for resolving complex issues

• Developed a robust CI/CD (Continuous Integration/Continuous Deployment) process to ensure thorough monitoring and smooth execution of builds. Data Scientist July 2017- Dec 2021

TechnipFMC, Hyderabad, India

• Developed machine learning models for predictive maintenance, reducing equipment downtime by 30% and saving $2M annually.

• Led digital transformation initiatives that integrated disparate, siloed data into the Subsea Studio platform, improving data accessibility by 70% and reliability by 50%.

• Performed SN (service notifications) data classification for subsea component using Natural Language Processing (NLP), leading the design improvement project based on field issues.

• Implemented unsupervised machine learning techniques to analyze and extract valuable insights from maintenance data for an internal product service.

• Conducted A/B testing to optimize fuel production processes.

• Collaborated with product engineers to integrate data science into pipeline design and optimization projects, leveraging Random Forest and Support Vector Machine models to predict potential pipeline failures.

• Implemented FMEA analysis by applying clustering methods to categorize equipment failure data, facilitating the computation of the Risk Priority Number (RPN), resulting in a 25% decrease in maintenance costs.

Data Analyst July 2015 - June 2017

TechnipFMC, Hyderabad, India

• Developed an algorithm for using excess inventory across the company, which saved approx.

$240,000/year on additional inventory management and increased delivery speed by 10%

• Digitized manufacturing/testing instructions, enabling client inspections for tailored solutions, and utilizing data to enhance equipment safety.

• Realized a cost saving of $28MM by leading the development of a cloud-based analytical tool utilizing predictive modeling to optimize drill parameters.

• Streamlined the process by integrating QN (quality notifications) data from SAP to PowerBI reducing the QN process lead time by 20%

• Created a global design database using SQL, helped in reducing the duplication of new product developments, saving $6.5 MM YoY.

• Streamlined conventional concept, FEED, and tender stages into ultra-fast digital field development during the field development stage, reducing the time taken in these stages by 40% Education

Master of Science: Data Science Jan 2022- May 2023 University of Texas at Dallas - Dallas, USA

Bachelor of Engineering Mechanical Engineering

Master of Science Economics Aug 2006-July 2011

Birla Institute of Technology and Science, Pilani- Goa, India Skill and Software

Programming languages and Tools

Python (NumPy, Pandas, SciPy,

Scikit-learn, Pytorch, Matplotlib),

SQL

Excellent

R, Spark, Scala, Matlab

Very Good

Airflow, Snowflake, MongoDB, Hive,

Azure Databricks, Scala, SAS

Programming

Good

Visualization Tools

PowerBI, Alteryx,

Tableau, MS Access,

Excel

Very Good

Cloud Services

Microsoft Azure, AWS, GCS

Good

Machine Learning

Regression, clustering, SVM, Decision

trees, Classification, Recommendation

systems, Time Series Analysis,

TensorFlow, Clustering Analysis

Good

Supervised leaning : Linear and Logistic

regressions, decision trees, support

vector machines, Random Forest,

Gradient Boosted Models

Unsupervised leaning: K- means

clustering, principal component

analysis(PCA)

Good

Certifications

AWS solution architect

(QE1J11JCSEVQQPGS)

Graduate in Applied Machine Learning

University of Texas at Dallas

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