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:
o Reasoning over structured and unstructured enterprise data
o Classification, extraction, and synthesis of procurement-related information
o 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:
o Regression and classification techniques
o Clustering and segmentation methods
o Feature engineering and model validation
o 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:
o Prompt design and structured prompting
o Embeddings and vector-based retrieval
o Retrieval-augmented generation (RAG) patterns
o 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:
o Modular code design
o Version control (e.g., Git)
o 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:
o API development frameworks (e.g., FastAPI)
o Cloud platforms (Azure preferred)
o 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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