FULL TIME | Applied AI & Data Science Engineer || NYC, NY (Hybrid)

Role: Applied AI & Data Science Engineer Location: NYC, NY (Hybrid) Fulltime Net-New AI & Analytics Solution Development (60–70%) • Identify, frame, and solve novel procurement and sourcing problems using data, analytics, and AI. • Design and develop end-to-end analytical and AI-driven POCs starting from loosely defined business questions. • Select and apply appropriate techniques across statistics, machine learning, optimization, and AI/LLMs based on problem context. • Rapidly iterate on hypotheses and solution approaches based on client and consultant feedback. • Produce clear, defensible analytical outputs that support executive decision-making. LLM & Advanced AI Applications (20–30%) • Design and implement AI solutions leveraging large language models for tasks such as: • Reasoning over structured and unstructured enterprise data • Classification, extraction, and synthesis of procurement-related information • Multi-step analytical or decision-support workflows • Evaluate AI solution performance across accuracy, explainability, reliability, and cost dimensions. • Ensure AI-driven outputs are transparent, interpretable, and appropriate for enterprise decision environments. Prototyping, Collaboration & Productization Readiness (10–20%) • Develop lightweight prototypes, demos, and analytical artifacts to support client workshops and solution validation. • Collaborate with engineering and product teams to ensure successful POCs are designed with a clear path to scalability and production readiness. • Contribute reusable analytical patterns, reference architectures, and accelerators that enable faster development of future AI solutions. What you’ll need Experience & background • 8–10 years of professional experience, with at least 3–4 years in applied AI, data science, or advanced analytics roles, delivering solutions in enterprise environments. • Demonstrated experience taking AI- or analytics-driven solutions from problem definition through prototyping, and in some cases into production or scaled deployment. • Prior exposure to procurement, sourcing, supply chain, manufacturing, or enterprise operations is strongly preferred. • Experience operating in consulting-style or client-facing environments, where requirements evolve and ambiguity is common. Core technical & analytical skills Data science & machine learning • Strong hands-on experience with Python and common data science libraries (e.g., pandas, numpy, scikit-learn). • Solid applied understanding of: • Regression and classification techniques • Clustering and segmentation methods • Feature engineering and model validation • Basic time-series or trend analysis • Ability to select appropriate analytical techniques based on business context rather than defaulting to complexity. Advanced analytics & decision modeling (preferred) • Familiarity with optimization, simulation, or scenario modeling techniques used in decision-support systems. • Experience translating analytical results into clear, defensible business insights. AI & LLM capabilities • Hands-on experience working with large language models (LLMs) and modern AI APIs. • Practical understanding of: • Prompt design and structured prompting • Embeddings and vector-based retrieval • Retrieval-augmented generation (RAG) patterns • Classification, extraction, summarization, and reasoning workflows • Experience designing AI solutions that combine LLMs with structured data, analytics, or rule-based logic. • Ability to evaluate AI outputs across accuracy, explainability, reliability, and cost, particularly for enterprise decision-making use cases. Prototyping, engineering & tooling • Experience building analytical and AI prototypes using notebook-first workflows (e.g., Jupyter). • Comfortable developing lightweight demo or exploratory applications (e.g., Streamlit, Gradio, or similar frameworks). • Familiarity with modern software development practices, including: • Modular code design • Version control (e.g., Git) • Basic API concepts and data pipelines Nice to have • Experience using LLM-assisted development tools (e.g., Cursor, GitHub Copilot, cloud-based coding assistants) to accelerate prototyping and iteration. • Exposure to: • API development frameworks (e.g., FastAPI) • Cloud platforms (Azure preferred) • Basic MLOps concepts such as model evaluation, monitoring, or deployment patterns • Experience collaborating with product or platform teams to transition POCs into scalable solutions. Professional skills • Strong analytical judgment and comfort operating in ambiguous, fast-paced client environments. • Ability to communicate complex analytical and AI concepts clearly to both technical and non-technical stakeholders. • Proven ability to collaborate effectively across consulting, product, and engineering teams. • High ownership mindset with a bias toward experimentation, iteration, and delivery. •