AI​/ML Specialist - Financial Services; Fraud, Risk & Forecasting

Join the Kumo Team

Kumo is building the next generation of AI for structured data. With our Relational Foundation Model, we help some of the world’s largest companies transform their data into predictions, decisions, and end-to-end automated systems.

Kumo is already used by many of the largest consumer banking and financial institutions in the world. Our culture is collaborative, fast‑moving, and deeply user‑obsessed. We value people who take initiative, learn quickly, communicate clearly, and enjoy solving hard technical + people challenges.

Fraud and financial prediction are no longer “flat table” problems. The highest‑value signals live in how entities connect and evolve over time: accounts ↔ devices ↔ merchants ↔ transactions, and the networks behind them.

Graph Transformers are enabling a generational leap in model quality on these highly connected datasets. The opportunity now is to bring that leap to the most sophisticated data science + ML teams in finance—teams that will pressure‑test everything (leakage, ablations, calibration, robustness, drift) and only adopt what stands up in the real world.

We’re hiring an Applied ML Specialist who can do both:

• Push the frontier of our core ML algorithms for financial use cases, and
• Work directly with customers to make those capabilities real, trusted, and deployed—then feed those learnings back into the product.

• Energized by high‑stakes predictive problems (fraud, risk, forecasting) where the “last mile” matters.
• Highly technical, with a research mindset and strong engineering instincts.
• Excited to be customer‑facing and own outcomes—because in a startup, the best product ideas come from the field.
• Motivated by leverage: the things you build become platform capabilities used across many deployments.

Support and eventually own technical success for strategic financial services customers adopting Kumo—and convert what you learn into improvements to the core platform.

• Own a domain (fraud/AML, credit risk, or forecasting): define “what good looks like,” build the evaluation plan, run the experiments, and drive adoption.
• Work hands‑on with large‑scale relational datasets and customer pipelines, with a focus on connected + temporal modeling.
• Design and execute rigorous model validation: leakage‑proof evaluation, calibration, robustness to drift/adversaries, and practical interpretability for real teams.
• Translate ambiguous customer needs into concrete modeling workflows and rollout plans.
• Partner closely with Kumo engineering and research to ship platform improvements informed by real customer constraints.
• Act as a technical leader and trusted advisor, understanding that deploying ML is as much a people and business challenge as it is a technical one.
• Deliver demos, workshops, best practices—and help drive pilots → production → expansions (including technical diligence during deal cycles).

• Fraud detection (rings, mule networks, ATO/CNP, abuse patterns)
• Credit scoring, risk modeling, and underwriting
• Relational forecasting across entities and time
• Financial customer analytics (propensity, retention, growth, risk‑aware marketing)

• Bachelor’s, Master’s or PhD in a STEM field (CS, EE, Math, Physics, Stats, etc.) or equivalent practical experience.
• Strong fundamentals in machine learning, statistics, and data science.
• Proven ability to improve ML systems end‑to‑end: data → modeling → evaluation → production constraints (not just notebooks).
• Solid engineering skills: proficient developing safe and correct code with the latest coding agents.
• Strong communication skills; comfortable navigating technical + non‑technical audiences.
• Motivated, self‑driven, excited to learn fast, and comfortable in a high‑velocity startup environment.

Deep expertise in one or more of:

• Fraud / AML / networked abuse detection (adversaries, class imbalance, delayed labels, investigations)
• Credit risk, scoring, underwriting, or lending analytics (calibration, stability, governance constraints)
• Forecasting at…