Applied AI Data Scientist - Consultant

Applied AI Data Scientist Contractor Location: Charlotte, NC (On-site, 5 days/week)

Role Type: Contractor / Consultant

Duration: 12 Months

TeckLeap is seeking an Applied AI Data Scientist who thrives at the intersection of analytics, engineering, and real-world problem solving. This role is ideal for someone who enjoys working hands-on with complex data, building production-grade AI capabilities, and contributing to high-impact applied intelligence initiatives.

Key Responsibilities

• Perform statistical analysis, clustering, and probability modeling to uncover insights and inform AI-driven solutions.
• Analyze graph-structured data to detect anomalies, extract probabilistic patterns, and support graph-based intelligence use cases.
• Build NLP pipelines focused on NER, entity resolution, ontology extraction, and scoring methodologies.
• Contribute to AI/ML engineering efforts by developing, testing, and deploying data-driven models and services.
• Apply ML Ops fundamentals, including experiment tracking, metric monitoring, and reproducibility best practices.
• Collaborate with cross-functional teams to translate analytical findings into scalable, production-ready capabilities.
• Prototype rapidly, iterate efficiently, and help evolve data science best practices across the team.

Basic Qualifications (Required)

• Degree in Computer Science, AI/ML, or a related technical field.
• 5+ years of AI/ML-focused software engineering experience.
• Strong experience in statistical modeling, clustering techniques, and probability-based analysis.
• Hands-on expertise in graph data analysis, including anomaly detection and distribution pattern extraction.
• Proficiency in Python and common data science/AI libraries (e.g., NumPy, Pandas, scikit-learn, PyTorch, spaCy, NetworkX).

Preferred Qualifications

• Solid NLP skills with practical experience in NER, entity/ontology extraction, and evaluation methods.
• An engineering-forward mindset with the ability to build, deploy, and optimize real-world solutions, not just theoretical models.
• Working knowledge of ML Ops fundamentals, including experiment tracking and key model performance metrics.
• Strong communication skills and the ability to collaborate effectively in fast-paced, applied AI environments.