Skills & Expertise
A comprehensive overview of my technical skills, tools, and domain expertise in data intelligence and AI.
Technical Skills
Large Language Models (LLMs)95%
Machine Learning95%
Deep Learning90%
Natural Language Processing85%
Computer Vision80%
Predictive Modeling95%
Statistical Analysis90%
Recommendation Systems85%
Neural Networks90%
Tools & Technologies
Machine Learning & AI
TensorFlowPyTorchScikit-learnKerasXGBoostLightGBMHugging FaceOpenAI APINLTKSpaCy
Big Data & Cloud
Apache SparkApache KafkaHadoopAWS (S3, EC2, EMR, SageMaker)Google Cloud (BigQuery, AI Platform)Azure (Data Factory, ML Studio)DatabricksSnowflakeDockerKubernetes
Data Visualization & BI
TableauPower BID3.jsMatplotlibSeabornPlotlyLookerGrafanaGoogle Data StudioKibana
Databases & Storage
PostgreSQLMySQLMongoDBCassandraRedisElasticsearchAmazon RedshiftGoogle BigQueryNeo4jDynamoDB
Domain Expertise
Finance & FinTech
- •Fraud Detection
- •Risk Modeling
- •Algorithmic Trading
- •Credit Scoring
- •Portfolio Optimization
- •Workforce Analytics
- •Operations Efficiency Modeling
- •Customer Acquisition Analytics
Telecommunications
- •Contact Center Analytics
- •Telecom Billing Optimization
- •Customer Experience Analytics
- •Revenue Analytics
- •Service Performance Metrics
SaaS & Technology
- •Go-to-MarketAnalytics
- •Business Intelligence and Visualization
- •Growth Analytics
- •Marketing Analytics
- •Sales Performance Analytics
- •Revenue Forecasting
- •Customer Success Metrics
- •Product Usage Analytics
- •Go-to-Market Analytics
- •Customer Acquisition Analytics
Government Procurement
- •Vendor Performance Analysis
- •Business Intelligence and Visualization
- •Competitor Analysis
- •Contract Optimization
- •Compliance Monitoring
- •Cost-Benefit Analysis
- •Procurement Process Automation
Continuous Learning
I'm committed to continuous professional development and staying at the forefront of emerging technologies in the rapidly evolving field of data intelligence and AI. This includes regularly:
- •Completing advanced courses and certifications in AI and data science
- •Participating in relevant conferences and workshops
- •Contributing to open-source projects
- •Reading research papers and technical literature
- •Conducting independent research and experimentation