Advanced Data Scientist

Summary

I am an advanced data scientist with expertise in developing and deploying end-to-end AI and machine learning solutions that deliver measurable business impact. My experience spans automating complex workflows, optimizing pricing systems, and designing scalable MLOps pipelines.

I have built multi-AI agent frameworks using Azure OpenAI, CrewAI, and transformer-based models to automate compliance processes and extract structured insights from unstructured data sources such as images, PDFs, and text. My work includes fine-tuning large language models and sentence transformers for domain-specific classification tasks, achieving over 95% accuracy and significant operational savings.

I have productionized multiple machine learning models using MLflow and Databricks, integrating with cloud ecosystems such as Azure, AWS, and GCP. My technical expertise covers Python, SQL, PyTorch, TensorFlow, and NLP frameworks like HuggingFace and NLTK. Ive also implemented pricing optimizers and customer segmentation models using XGBoost and other ensemble methods, improving prediction accuracy and profitability.

Beyond model development, I focus on scalable AI governance and observability, tracking performance metrics, automating retraining, and enabling efficient collaboration between data scientists and MLOps engineers. My goal is to design intelligent, multi-agent AI systems that drive automation, reduce risk, and accelerate decision-making at scale.

Expectations

In my next role as a Senior Data Scientist, I am seeking opportunities where I can lead the design and deployment of large-scale AI and machine learning systems that deliver measurable business outcomes. I want to work on projects that go beyond experimentation, focusing on building production-grade GenAI and multi-agent solutions powered by LLMs, transformers, and automation frameworks. Im particularly interested in roles that combine technical depth with strategic influence, allowing me to collaborate cross-functionally with product and engineering teams to translate data insights into scalable AI products. I also value environments that emphasize strong MLOps practices, leveraging tools like Databricks, MLflow, and cloud ecosystems such as Azure, AWS, or GCP to ensure reliability, transparency, and efficiency in model lifecycle management. Above all, Im looking for a role that supports continuous learning, innovation, and ownership, where I can contribute to shaping impactful AI initiatives end to end.

Employment Preferences

Expected Base Salary

**0,000 USD

Expected Total Compensation

**0,000 USD

Academic Degree

Experience

Skills

• Python
• SQL
• R
• C++
• Java
• Machine Learning
• Deep Learning
• Natural Language Processing
• Generative AI
• Large Language Models
• Multi-AI Agent Frameworks
• CrewAI
• Azure OpenAI
• HuggingFace
• TensorFlow
• PyTorch
• Scikit-learn
• NLTK
• MLflow
• Databricks
• MLOps
• Data Engineering
• Data Science
• Model Deployment
• Model Optimization
• Predictive Modeling
• Feature Engineering
• NLP Pipelines
• Text Mining
• Transformer Models
• Fine-tuning
• Prompt Engineering
• Cloud Computing
• Azure
• AWS
• GCP
• Snowflake
• Apache Spark
• Hadoop
• XGBoost
• Random Forest
• Time Series Forecasting
• Computer Vision
• Image Processing
• Compliance Automation
• Risk Analytics
• Pricing Optimization
• AI Automation
• Business Intelligence
• Data Visualization
• Tableau
• Alteryx
• Jupyter
• Pandas
• NumPy
• OpenCV
• Responsible AI
• AI Governance
• Model Monitoring
• Few-shot Learning
• Reinforcement Learning