Bio

📌 ''In God we trust; all others must bring data.'' Data/ ML practitioner with 6+ years of industry experience and a unique blend of data engineering, machine learning, and business analysis expertise. In my professional experience, I have worked on end-to-end analytics projects that involved Data Analysis, Data Engineering, Data Visualization, Data Modeling, Machine Learning Model Deployment, and Analytics Framework Development for solving business problems. Currently, as a Data Engineer at Applied ML Search team at Apple, I'm building a search metrics data store for retail/e-commerce data, and developing automated training pipelines for search query intent classification model and learn-to-rank ML model in production. Most recently, as an AI Engineer at IproTech, I worked on a custom NLP model for Named Entity Recognition that identifies and redacts personally identifiable and sensitive information from unstructured text documents. Prior to that, I worked as a Data Engineer at Nike creating robust and scalable enterprise data pipelines for the Consumer Data Analytics & Engineering (CDEA) team. ✅ Programming: Python (Numpy, Pandas, NLTK, Spacy, Scikit-Learn), PySpark, SQL, Tableau. ✅ Big Data Tools & Frameworks: Apache Spark, Hadoop, ETL data pipelines, Apache Airflow, Apache Oozie, Hive, AWS (EMR, S3, EC2, Lambda, SNS, Glue, Redshift), lakeFS ✅ VCS, DevOps & Misc Tools: PyCharm, Git, Jira, Jenkins, Docker ✅ Statistics: Inferential Statistics, Experimental Design, Hypothesis Testing (A/B Testing), Regression Analysis ✅ Machine Learning: Regression Modeling, Random Forest, XGBoost, kNN Classifier, K-means Clustering, Feature Extraction (PCA, Factor Analysis), Natural Language Processing (Text Analytics – PII, PHI Extraction), Convolutional Neural Network. ✅ Business Domain Expertise: Enterprise Data Analytics (Data Warehousing), Sales and Marketing Analytics, Customer Segmentation, Customer Lifetime Value and Retention Analysis, Customer Success KPIs, Product Analytics Reach me at: ✉️ vinodhini.sd@asu.edu