Data Scientist

Job description

**Machine Learning & AI Model Development:** - Design, build, and deploy **ML models** for real-world applications. - Implement **predictive analytics, deep learning, and statistical models** to solve complex problems. - Optimize models for **performance, scalability, and efficiency**. **Natural Language Processing (NLP) & GenAI:** - Work on **NLP-based AI solutions** such as **information extraction, text summarization, and conversational AI**. - Develop **LLM/SLM-powered AI pipelines** for **context-aware decision-making**. - Enhance **GenAI models** with **enterprise data integration and continuous learning**. **Computer Vision & Image Processing:** - Implement **AI-driven Computer Vision models** for **object detection, segmentation, and anomaly detection**. - Develop **OCR-based solutions** to extract and analyze information from **handwritten and structured documents**. **Data Engineering & Processing:** - Handle large-scale **structured and unstructured datasets** from various sources. - Develop **data pipelines, ETL processes, and feature engineering techniques**. **Enterprise Integration & AI Deployment:** - Integrate AI models with **enterprise platforms** (SAP, PLM, RPA, etc.). - Ensure seamless deployment of **AI-powered applications** into production. **Key Skills & Qualifications** **Technical Skills:** - Strong programming skills in **Python** (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch, Hugging Face). - Experience with **AI/ML, NLP, Computer Vision, and GenAI**. - Knowledge of **LLMs, SLMs, RAG, and multi-agent AI orchestration**. - Hands-on experience with **cloud platforms (AWS, Azure, GCP)**. - Experience with **MLOps tools, model monitoring, and performance optimization**. **Soft Skills:** - **Team Player** – Ability to work collaboratively in cross-functional teams. - **Problem-Solver** – Passion for solving complex AI-driven challenges. - **Innovative Mindset** – Willingness to explore new AI/ML techniques and push boundaries. - **Strong Communication** – Ability to present technical concepts to non-technical stakeholders. - **Self-Starter** – Proactive and result-oriented approach. environments, and communicating results.