Ai Engineer / Data Scientist

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.