AI Data Scientist-Onsite @Charlotte, NC or Dallas, TX-Need Locals

Job Title: AI Data Scientist Location: Charlotte, NC or Dallas, TX Work Model: 100% Onsite Job Summary We are seeking an experienced AI Data Scientist with deep expertise in statistical analysis, graph-based data modeling, NLP, and end-to-end ML engineering. This role requires a strong engineering mindset, with the ability to build, train, deploy, and scale advanced AI/ML solutions in a production environment. The ideal candidate combines analytical rigor with hands-on data engineering and ML Ops capabilities. Key Responsibilities Core Data Science & AI • Perform advanced statistical analysis, hypothesis testing, and A/B experimentation to drive data-driven insights. • Design and build machine learning, deep learning, and AI models across classification, regression, forecasting, clustering, and optimization. • Develop and apply Graph Analytics (network analysis, graph embeddings, knowledge graphs, graph neural networks). • Build production-grade NLP models for text classification, entity extraction, semantic search, embeddings, summarization, and LLM-based applications. ML Engineering & Operations • Work hands-on to build, train, optimize, and deploy ML models into production using ML Ops frameworks. • Implement CI/CD pipelines for ML workflows, model monitoring, versioning, and automated retraining. • Build scalable data pipelines in collaboration with engineering teams. Data Engineering Support • Work with structured, semi-structured, and unstructured data. • Build data ingestion and transformation workflows supporting feature engineering. • Partner with data engineering teams to ensure high data quality and model readiness. Cross-Functional Collaboration • Work closely with product, engineering, architecture, and business teams to turn business problems into scalable AI/ML solutions. • Communicate complex quantitative findings to technical and non-technical stakeholders. Required Skills & Experience • 10+ years of professional experience in Data Science, AI, or Applied Machine Learning. • Strong foundation in statistics, probability, experimental design, and quantitative modeling. • Hands-on expertise with Graph Data Analysis/Graph ML (e.g., Neo4j, NetworkX, TigerGraph, GraphFrames). • Deep proficiency in NLP techniques and modern frameworks (Transformers, Hugging Face, spaCy, BERT/LLMs). • Proven experience with ML Ops tools (MLflow, Kubeflow, SageMaker, Vertex AI, Airflow, etc.). • Strong engineering mindset with hands-on development in: • Python (Pandas, NumPy, SciPy, PyTorch/TensorFlow, Scikit-learn) • Model deployment (Docker, Kubernetes, APIs) • Experience building end-to-end ML systems from concept to production deployment. • Understanding of cloud environments (AWS, Azure, or GCP). • Strong communication and documentation skills. Preferred Qualifications • Experience deploying LLM-based applications in production. • Experience with knowledge graphs, graph neural networks (GNNs), or graph embeddings. • Experience with real-time model serving or streaming data platforms (Kafka, Kinesis). • Background in financial services, banking, insurance, or other regulated industries (nice to have). Regards, Sai Srikar 7704565690 Email: sai@cysphere.net