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.

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