Naim Katiman

Data Scientist

https://www.opentalent.in/naim-katiman

Either coding or hustling

RM Investment Bank Ltd

Key Strengths

Strong foundational knowledge in Python for data science and machine learning, evidenced by multiple personal projects.
Practical experience with various AI/ML frameworks and libraries (CrewAI, Langchain, TensorFlow, Scikit-learn).
Demonstrated ability to build and deploy AI-driven applications, including trading signals and RAG pipelines.
Exposure to full-stack development concepts, which can be beneficial for deploying data science solutions.
Proactive in learning and applying new technologies, as seen in projects involving Llama 3.1 and multi-modal RAG.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects show a strong interest in cutting-edge AI/ML technologies, particularly in areas like RAG, LLMs, and AI agents. This aligns well with an innovative and research-driven culture. The diversity of projects, from trading systems to memory agents, indicates a broad curiosity. However, the lack of team-based projects or contributions to open-source initiatives makes it difficult to assess collaboration and cultural integration fully. The target role is Data Scientist, and the projects are highly relevant, indicating a good fit for the technical aspects of the role.

Soft Skills & Operational Fit

The candidate's project descriptions indicate a self-starter attitude and a drive for continuous learning. The focus on personal projects suggests strong intrinsic motivation. However, without psychometric test results or interview data, it's difficult to assess specific soft skills like teamwork, stress handling, or communication clarity in a professional setting.

AI is analyzing your overall score…

Identifying your key strengths…

Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Projects

continuous-improvement

April 4, 2026 – Present

Agents that gets sharper every session

View Project

tradeclaw-agent

March 25, 2026 – Present

🦞 Self-hosted AI trading signal agent. Runs on your machine. Delivers signals to Telegram, Discord, or any webhook. Free forever.

View Project

tradeclaw

March 21, 2026 – Present

🤖 Self-hosted AI trading signals - 5 swappable strategy presets (Classic, HMM, regime-aware, VWAP+EMA+BB, Full-Risk Pipline), multi-preset backtest comparison, paper trading, Telegram bot. BTC, ETH, Gold, Forex. Free forever.

View Project

OpenMemory

March 1, 2026 – Present

TypeScript-powered AI memory that actually works. No more split personalities, no more lost context. Just pure memory continuity.

View Project

TextGoogleSheetEditor

December 3, 2024 – December 3, 2024

C# Test with ASP.Net MVC

View Project

RAG-using-Llama-3.1-WebUi-on-Streamlit

August 6, 2024 – August 6, 2024

Run Llama on serverless with multimodal RAG, with focus on reading files (But with limited token ofc)

View Project

Multi-Modal-RAG-Pipeline-on-Images-and-Text-Locally

August 5, 2024 – August 5, 2024

My first Multi-Modal RAG pipeline....Dummy version

View Project

Llama-3.1-8B-Free-Version

July 31, 2024 – July 31, 2024

Set up Ollama locally with a QA component using lightrag library on Windows.

View Project

Stock-Analysis-As-Agents-NVDA

July 18, 2024 – July 18, 2024

This project uses the CrewAI framework to automate stock analysis, enabling AI agents to collaborate and execute complex tasks efficiently. Example stock: Nvidia. Technologies include Python, CrewAI, Unstructured, PyOWM, Tools, Wikipedia, yFinance, SEC-API, tiktoken, faiss-cpu, python-dotenv, langchain-community, langchain-core, and OpenAI.

View Project

FullstackBootcamp

July 15, 2024 – April 1, 2025

During the boot camp, I explored web development (HTML, CSS, JavaScript, Vue, React, PHP, Laravel, Flask, Django, WordPress), backend (MySQL, MongoDB, Firebase, Redis, Node.js), DevOps (Linux, Git, Docker, Apache Kafka), cloud computing, machine learning (Python, TensorFlow, Scikit-learn), RESTful APIs, and agile methodologies.

View Project

Key Strengths

Strong foundational knowledge in Python for data science and machine learning, evidenced by multiple personal projects.
Practical experience with various AI/ML frameworks and libraries (CrewAI, Langchain, TensorFlow, Scikit-learn).
Demonstrated ability to build and deploy AI-driven applications, including trading signals and RAG pipelines.
Exposure to full-stack development concepts, which can be beneficial for deploying data science solutions.
Proactive in learning and applying new technologies, as seen in projects involving Llama 3.1 and multi-modal RAG.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Naim Katiman

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Experience

Projects

Key Strengths

Cultural & Operational Fit