Ranganath C

About Ranganath C

I am a results-driven Data Scientist with over 7 years of experience designing, developing, and deploying advanced machine learning models and scalable data solutions across diverse industries. Currently working with Nike Inc, I specialize in building end-to-end ML pipelines, real-time analytics, and big data processing using cutting-edge technologies such as Python, R, PySpark, AWS, and Azure.My expertise spans the entire data lifecycle—from data ingestion and cleaning to predictive modeling, NLP, and insightful visualization. I have a proven track record of leveraging tools like Apache Spark, Kafka, Hive, and cloud platforms (AWS SageMaker, Glue, EMR; Azure Data Factory, Databricks, ADLS) to deliver business-critical insights and optimize decision-making processes.At Fidelity Investments, I led the development of enterprise data platforms and implemented scalable NLP and real-time streaming analytics solutions, enhancing operational efficiency and data-driven strategy. My work emphasizes automation, CI/CD best practices, and agile collaboration with cross-functional teams, including business analysts and data architects.With a strong foundation in statistical modeling, deep learning, and data engineering, I am passionate about transforming complex data into actionable intelligence. I thrive in fast-paced environments where innovation and precision drive success and look forward to leveraging my skills to solve challenging business problems and create impactful AI solutions.

Experience

1. Data Scientist

   Nike

   Dec 2023 — Present · Beaverton, OR, US

   Developed and deployed ML models using Linear/Logistic Regression, k-NN, SVM, Decision Trees, Random Forest, XGBoost, and K-Means to address key business challenges- Performed end-to-end statistical analysis and data wrangling on large datasets using Python, R, and SQL to improve data quality and extract insights- Built predictive models in R with XGBoost; validated results using statistical techniques and probability estimates- Created scalable data pipelines using PySpark, Hive, and Spark Streaming to process structured and real-time data from HDFS/HBase- Utilized Apache Kafka for live stream processing and delivered real-time analytics dashboards for business decision-making- Developed ML pipelines using AWS Glue for ETL and Step Functions to automate workflows triggered by S3 events- Deployed models through SageMaker, storing predictions and inference outputs in S3; leveraged AWS Batch for feature engineering- Built and managed Spark clusters on AWS EMR for large-scale distributed processing and storage optimization- Led POC integrating Apache Spark with Cassandra to evaluate scalable NoSQL-based analytics- Applied NLP using NLTK to process unstructured data and implemented Neural Topic Modeling (NTM) on SageMaker- Designed and maintained Tableau dashboards integrated with Hive/MySQL; visualized data using Matplotlib and Seaborn- Executed SQL scripts, stored procedures, and triggers for advanced data manipulation and reporting- Automated reporting using Excel with macros, advanced formulas, and PivotTables- Executed ETL jobs with Hive scripts and Bash; used Apache Flume for ingesting web logs into HDFS- Followed Agile methodologies; contributed to sprint planning, daily stand-ups, JIRA tracking, and documentation in Confluence- Used Git/GitHub for version control and Python Notebook for collaborative analytics and development.

Education

• Narayana Junior College - India

  Intermediate, PCM

  2011 — 2013

• The University of Texas at Dallas

  Master of Science in Business analytics, Business

• Kendriya Vidyalaya

  Primary Education

  2001 — 2008

• GITAM Deemed University

  Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering

  2013 — 2017

• Viswasanthi English Medium High School - India

  Secondary Education, 10th Grade

  2008 — 2011