Senior Data Scientist – Conventional AI & Generative AI Specialist

Data Collection & Cleaning: Collect, preprocess, and ensure data quality across large datasets, implementing best practices in data cleaning and integrity management. Feature Engineering & Data Transformation: Design and extract relevant features from raw data to optimize machine learning model performance and ensure the best data representation. Data Analysis & Predictive Modeling: Utilize statistical and machine learning techniques to identify trends, patterns, and correlations in data, and build predictive models for forecasting and decision support. · Hypothesis Testing & Insights Generation: Formulate and test hypotheses to derive actionable insights, providing data-driven recommendations for business strategies. Data Visualization & Reporting: Develop intuitive data visualizations, dashboards, and reports using tools like Matplotlib, Seaborn, Plotly, Power BI, or Tableau to communicate findings effectively to both technical and non-technical stakeholders. Generative AI Model Development: Train and fine-tune generative AI models using state-of-the-art techniques such as LoRA/QLoRA, and optimize these models for real-world applications. Agentic AI & Retrieval-Augmented Generation (RAG): Design, develop, optimize, and deploy Agentic AI systems and RAG models to facilitate intelligent information retrieval and generate contextually relevant outputs. Vector Database Management & Information Retrieval: Leverage vector databases and advanced indexing techniques to enable efficient storage and retrieval of relevant data, particularly for conversational contexts. API & Microservices Development: Build and maintain scalable APIs and microservices to facilitate AI model integration into applications and enterprise solutions. Model Deployment & Optimization: Deploy machine learning models into production environments, ensuring seamless integration with existing pipelines and real-time processing capabilities. MLOps & Automation: Implement and manage MLOps best practices, including CI/CD pipelines, model versioning, monitoring, and automated retraining to maintain performance and reliability. Collaboration & Decision Support: Work closely with cross-functional teams, including product managers, engineers, and stakeholders, to deliver data-driven insights and support decision-making processes. Data Privacy & Security: Ensure all data handling practices comply with relevant privacy and security regulations, protecting both sensitive data and intellectual property. Continuous Learning & Innovation: Stay current with the latest advancements in AI and data science, applying new methodologies and tools to enhance model performance and business outcomes.