Data Scientist

Summary

Data Scientist with Masters degree in Applied Machine Intelligence and 5+ years of experience in developing advanced machine learning models and deploying scalable solutions. Expertise in data analysis, image processing, and natural language processing (NLP) with a strong focus on improving operational efficiencies and driving business insights. Proficient in Python, SQL, TensorFlow, and cloud platforms (AWS, GCP, Azure). Proven track record of delivering impactful solutions for multinational organizations.

Expectations

I want a work environment with opportunities for Innovation and Growth, where I can leverage my skills in machine learning, AI, and data science while having opportunities for continuous learning and development. I also want a collaborative team where I can learn from my peers and mentors.

Employment Preferences

Expected Base Salary

**0,000 USD

Academic Degree

Experience

Skills

• Data Analyst
• Data Scientist
• Machine Learning
• Applied Machine Intelligence
• AI
• Artificial Intelligence
• NLP
• Natural Language Processing
• Deep Learning
• CNN
• Convolutional Neural Networks
• BERT
• Transformers
• LLMs
• Large Language Models
• MLOps
• Data Analysis
• Data Mining
• Image Processing
• Python
• SQL
• TensorFlow
• PyTorch
• Keras
• GCP
• AWS
• Azure
• Cloud Platforms
• Big Data
• PySpark
• Docker
• Kubernetes
• Flask
• FastAPI
• Object Detection
• YOLO
• Sentiment Analysis
• Classification
• Clustering
• Regression
• Neural Networks
• Feature Extraction
• Data Modeling
• Statistical Analysis
• A
• B Testing
• OCR
• Tesseract
• Computer Vision
• Event Clustering
• ESG Scoring
• Document Parsing
• Data-Driven Solutions
• Automation
• Edge Devices
• Business Insights
• Predictive Modeling
• Time Series Analysis
• Reinforcement Learning
• Anomaly Detection
• Dimensionality Reduction
• Hyperparameter Tuning
• Bayesian Inference
• Feature Engineering
• Cross-Validation
• Model Evaluation
• Data Wrangling
• Data Preprocessing
• Data Imputation
• Ensemble Learning
• Random Forest
• XGBoost
• Feature Selection
• KNN
• T-test
• Chi-square Test
• K-Means Clustering
• Gradient Boosting
• Decision Trees
• GitHub
• Apache Spark
• Hive
• Airflow
• Jupyter Notebooks
• REST APIs
• Microservices
• NoSQL Databases
• MongoDB
• Cassandra
• CI
• CD Pipelines
• Terraform
• Hadoop
• CloudFormation
• AWS Lambda
• S3 Buckets
• BigQuery
• KPI Tracking
• ROI Analysis
• Financial Modeling
• AIOps
• Fraud Detection
• Risk Management
• Customer Segmentation
• Marketing Analytics