AI Data Scientist @ w2

AI Data Scientist
Charlotte, NC or Dallas, TX
Long Term
Contract

• 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.

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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:

o Python (Pandas, NumPy, SciPy, PyTorch/TensorFlow, Scikit-learn)

o Model deployment (Docker, Kubernetes, APIs)

• Experience building end-to-end ML systems from concept to production deployment.
• Understanding of cloud environments (AWS, Azure, or Google Cloud Platform).
• Strong communication and documentation skills.

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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).

Munesh

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CYBER SPHERE LLC