Data Scientist & AI Specialist (NLP, AI, Business Intelligence)

Overview

We are seeking a highly analytical and research-driven Data Scientist & AI Specialist to develop and deploy AI-powered features, recommendation systems, and business intelligence solutions. This individual will work on cutting-edge NLP models, machine learning pipelines, and data-driven analytics to optimize both user experiences and strategic decision-making.

The ideal candidate is not only a strong technical expert in AI, NLP, and machine learning but also a problem solver who understands business intelligence and data science applications. They should be eager to research new methodologies, experiment with state-of-the-art techniques, and continuously optimize models to ensure real-world impact.

This role requires versatility, adaptability, and an ownership mindset, with a focus on building scalable AI solutions and deriving actionable insights from complex data.


Key Responsibilities

1. AI & NLP Feature Development

• Develop and fine-tune NLP models and AI-driven features for a Python-based application.
• Work with large language models (LLMs), retrieval-augmented generation (RAG), knowledge graphs, and semantic search techniques.
• Design and develop recommendation system algorithms that incorporate multiple factors, such as user interactions, performance data, and behavioral patterns.
• Implement LLM fine-tuning, prompt engineering, and text-processing pipelines for AI-enhanced user interactions.
• Build robust evaluation frameworks to assess model reliability, efficiency, and accuracy.

2. Business Intelligence & Machine Learning

• Collect, clean, and analyze structured and unstructured data from various sources.
• Design and maintain data models and ETL pipelines, ensuring data integrity, accessibility, and consistency.
• Develop interactive dashboards and data visualizations (Plotly, Dash, Streamlit, or BI tools) to support strategic decision-making.
• Apply machine learning and neural network-based approaches to improve forecasting accuracy, pattern recognition, and process optimization.
• Transform raw data into actionable insights, optimizing workflows and improving business strategies.

3. Model Deployment & MLOps

• Work closely with DevOps & Cloud Engineers to deploy AI models in cloud-based or local environments.
• Implement model versioning, automated retraining, and continuous monitoring to maintain high model performance over time.
• Optimize AI pipelines for scalability, efficiency, and low-latency inference, ensuring smooth integration with real-world applications.

4. Research & Continuous Improvement

• Stay up to date with state-of-the-art advancements in NLP, machine learning, and AI research.
• Conduct experiments with new algorithms, architectures, and frameworks to enhance AI capabilities.
• Document methodologies and communicate findings clearly to both technical and non-technical stakeholders.
• Identify opportunities to improve existing AI systems, refining models based on user feedback, performance data, and real-world interactions.

5. Collaboration & Documentation

• Work with developers, UI/UX designers, and cloud engineers to integrate AI solutions into a seamless user experience.
• Write technical documentation on AI models, data pipelines, and research methodologies.
• Present findings and translate complex AI concepts into actionable recommendations for decision-makers.