Adal Kelly
Development
TX, United States
Skills
Python
About
Adal Kelly's skills align with IT R&D Professionals (Information and Communication Technology). Adal also has skills associated with Database Specialists (Information and Communication Technology). Adal Kelly has 8 years of work experience.
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Work Experience
Data Scientist
Pano AI
January 2018 - December 2020
- Lead data mining and predictive modeling projects using R, Python, and Apache Spark to analyze large datasets. Implement advanced algorithms in machine learning (like Random Forest, SVM, and Neural Networks) to forecast sales and optimize inventory management. Use AWS cloud services (S3, E C2, E MR) for handling scalable data storage and compute-intensive machine learning tasks. Develop data visualization dashboards using Tableau and Power BI to present insights to non-technical stakeholders. Collaborate with the IT team to ensure robust cybersecurity measures in data handling and model deployment. Impact: E nabled the company to reduce inventory costs by 20% and improve demand forecasting accuracy by 35%.
Machine Learnning Engineer
10Pearls
January 2016 - December 2018
- Design and implement real-time analytics solutions using Python, Kafka, and Spark Streaming for processing streaming data from IoT devices. Apply machine learning models for predictive maintenance, reducing equipment downtime and operational costs. Utilize TensorFlow and Keras for developing deep learning models to enhance manufacturing process optimization. Work with data engineering teams to integrate ML models into production environments using containerization tools like Docker and Kubernetes. Perform rigorous model validation and testing to ensure high accuracy and reliability of predictions. Impact: Achieved a 25% reduction in equipment downtime and a 20% decrease in maintenance costs, significantly improving operational ešciency.
Senior ML Engineer & Data Scientist
January 2020 - Present
- Create and apply models and algorithms for AI/ML to address issues. Develop and deploy machine learning models using Python, TensorFlow, and Scikit-learn, focusing on improving customer recommendation systems. Utilize SQL and NoSQL databases for data querying and management. Implement natural language processing (NLP) techniques to analyze customer feedback, enhancing the accuracy of sentiment analysis models. Collaborate with engineering teams to integrate ML models into the company's web platform using Flask or Django for Python. Conduct A/B testing and statistical analysis to measure the impact of new models on customer engagement and sales. Impact: Improved the recommendation system accuracy by 30%, leading to a 15% increase in user engagement and a 10% increase in sales.
