Applied AI Scientist, GenAI and ML Prototyping

More than a mission, C2FO is a better financial system changing the way every business gains access to the working capital they need to thrive. At C2FO, everyone is an employee-owner which means we’re all invested in our work and team members. We’re a company of team players and self-starters finding new and innovative ways to get things done. If you’re excited to learn, grow, and leave your mark on our fast-growing organization, C2FO may be the place for you.

About C2FO
Headquartered in Kansas City, USA, C2FO has more than 500 employees worldwide, with operations throughout North America, Europe, India, Asia Pacific, and Australia. C2FO is the world’s largest on-demand working capital platform. Our mission is to ensure every business has the capital needed to thrive and we have delivered more than $400+ billion in funding to businesses since our founding. How do we do this? By providing fast, flexible, and equitable access to low-cost capital through our easy-to-use platform.We provide technology with a human touch, giving our customers the direct support they need and ensuring our team members have the tools, resources, and work environment they need to deliver on our promise to customers. With the C2FO platform, businesses worldwide have more working capital to fuel their growth, create jobs and develop new products.

Benefits
At C2FO, we take care of our customers and our people – the vital human capital that helps our customers thrive. That’s why we offer a comprehensive benefits package, flexible work options for work/life balance, volunteer time off, and more. Learn more about our benefits here. (https://www.c2fo.com/amer/us/en-us/about-us/careers

Designation: Applied AI Scientist, GenAI and ML Prototyping

Experience: 4-6 years

Location: REMOTE

Role Overview

• We are looking for a Senior Data Scientist to lead the identification and rapid prototyping of AI solutions across our business — spanning both internal operations and customer-facing products.

• This role sits at the earliest and most critical stage of our AI delivery lifecycle: Discovery and Proof of Concept. You will work directly with department heads and product owners to uncover where AI can create meaningful impact, then design and build working prototypes that demonstrate clear, measurable value. You will own the process from problem framing through to a validated, decision-ready POC — determining whether the right solution is a rule-based system, a traditional machine learning model, or an LLM-based agentic workflow.Once a prototype is approved, you will work in close collaboration with our AI Engineering team to ensure a clean handoff — translating your work into something they can scale into a production-grade application. You will not own productionisation, but you will be a critical partner in making it successful.

• This is a role for someone who is energised by ambiguity, moves fast without cutting corners, and knows how to make a compelling case for (or against) a technical approach based on evidence rather than enthusiasm.

Core Responsibilities

• Business Discovery Run structured discovery sessions with department heads and product owners to identify and scope AI opportunities. Define a clear problem statement — including data availability and constraints — before any prototyping begins.

• Rapid Prototyping Build functional POCs using the most appropriate approach for the problem: RAG pipelines, agentic workflows, predictive ML models, or rule-based systems. Prototypes must be credible enough to support a genuine build-or-not decision.

• Stakeholder Management Act as the primary technical point of contact for business stakeholders throughout discovery and POC. Communicate trade-offs around accuracy, cost, and latency in plain terms — and be willing to recommend against building when the evidence calls for it.

• Evaluation & Validation Define success criteria before building begins. Design and run evaluations appropriate to the POC type, and present findings clearly enough for a non-technical sponsor to make a confident go/no-go decision.

• Technical Handoff Produce handoff documentation covering system design, prompt strategies, data requirements, known failure modes, and evaluation benchmarks — giving the AI Engineering team everything needed to take a validated POC into production.

Tech Stack & Technical Requirements

Core Languages & Frameworks

• Proficiency in Python as the primary language for data science and ML development (Pandas, NumPy, Scikit-learn)

• Familiarity with SQL for data querying and manipulation across modern data warehouses (e.g., BigQuery, Snowflake, PostgreSQL)

• (Nice to have) Working knowledge of deep learning frameworks such as PyTorch or TensorFlow for model experimentation

LLM & Generative AI Tooling

• Hands-on experience working with large language model APIs, including providers such as OpenAI (GPT-4o), Anthropic (Claude), or Google (Gemini)

• Strong command of prompt engineering techniques, including few-shot prompting, chain-of-thought reasoning, and structured output design

• Experience with open-source LLMs (e.g., Mistral, LLaMA) and an understanding of when to apply open vs. proprietary models

Agentic Orchestration & RAG

• Practical experience building RAG (Retrieval-Augmented Generation) pipelines, including chunking strategies, embedding models, and retrieval tuning

• Familiarity with agentic orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen

• Experience integrating vector databases (e.g., pgvector, Pinecone, Weaviate, ChromaDB) into search and retrieval workflows

• Understanding of tool/function calling patterns for LLM-driven automation

Evaluation & Experimentation

• Ability to define and implement "good enough" metrics and evaluation frameworks for POC validation

• Experience with LLM evaluation libraries such as RAGAS, TruLens, or DeepEval

• Familiarity with experiment tracking tools such as MLflow or Weights & Biases

• Comfort with cost and latency profiling of LLM-based systems to inform feasibility decisions

Data & Infrastructure

• Comfortable working within cloud environments (AWS, GCP, or Azure) for data access, compute, and API integration

• Ability to integrate with REST APIs and third-party data sources during prototyping

• Proficiency with standard development tools: Git, Jupyter notebooks, VS Code

• Basic familiarity with Docker for packaging and sharing POC environments with engineering teams

Required Experience

• 4+ years of experience in data science, machine learning, or a closely related field, with a demonstrated track record of delivering end-to-end projects

• 2+ years of hands-on experience working with large language models or Generative AI solutions in a professional setting

• Proven experience taking projects from business problem discovery through to a working prototype or proof of concept

• Experience engaging directly with non-technical business stakeholders to gather requirements, set expectations, and communicate results clearly

• Strong background in traditional ML approaches (classification, regression, clustering, NLP) alongside modern LLM-based methods

Education

• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field

• A Master's or PhD is a plus, though equivalent industry experience is equally valued

Soft Skills & Ways of Working

• Ability to translate complex technical outputs into clear business value — you are as comfortable in a boardroom as you are in a notebook

• Strong stakeholder management skills, including the ability to set realistic expectations around LLM capabilities, limitations, and cost trade-offs

• Excellent written communication skills for documenting prompt strategies, data requirements, and POC logic to enable clean technical handoffs

• Self-directed with a high tolerance for ambiguity — you are energised by open-ended discovery, not slowed down by it

• Structured thinker who can design evaluation criteria and define what "success" looks like before building begins

Nice to Have

• Experience with fine-tuning or instruction-tuning LLMs on domain-specific datasets

• Familiarity with responsible AI principles, including bias detection, fairness evaluation, and model transparency

• Prior experience in a consulting, pre-sales engineering, or business-facing technical role

• Knowledge of business process mapping (e.g., BPMN) to support structured discovery sessions

• Open-source contributions, published research, or public work demonstrating applied AI expertise