Senior Data Scientist, Applied Machine Learning

About Enlighten

At Enlighten, we believe AI should expand what people can take on. We build teams of AI agents alongside Fortune 500 companies so they can put their best thinking to work across the business. We want the people closest to the work to be able to ask hard questions and get answers they can trust. Our mission is to give every team the intelligence it needs to make decisions that shape the business.

About the role

You'll build models that solve problems inside real business workflows. You’ll direct AI agents to prototype models and diagnose failures, set the criteria a model must meet, and work with engineers to get it into production.

What you'll do

• Define the target and how you'll measure success for prediction, ranking, classification, or anomaly detection.
• Build training and evaluation datasets that account for labels, leakage, sampling, timing, and the people or systems the model will serve.
• Compare candidate models with simple baselines. Inspect where each model fails, how well its probabilities are calibrated, and what those errors cost the business.
• Run multiple AI agents in parallel every day to write and test modeling code and build custom apps for examining predictions and errors. Set explicit acceptance checks and verify performance on representative data.
• Work with engineers on reproducible deployments, monitoring, and fallbacks when inputs or performance change.
• Explain where the model can be trusted, where people still need to make the call, and which changes are worth testing next.

What you bring

• A record of delivering applied machine learning into a business workflow, including evaluation after the initial prototype.
• Strong Python and SQL skills and experience with data preparation, feature engineering, and model evaluation.
• The ability to diagnose leakage, overfitting, poor labels, distribution changes, and misleading aggregate metrics.
• Practical understanding of reproducibility, deployment interfaces, and monitoring requirements.
• Hands-on use of multiple AI agents and the ability to build custom evaluation or diagnostic apps while checking generated work.
• Clear communication about model behavior, uncertainty, and business value.
• An independent, pragmatic working style with ownership from problem framing through a maintainable handoff.
• Experience with consumer or operational datasets, ranking systems, or anomaly detection.
• Experience with cloud ML platforms and production data pipelines.
• Experience combining statistical models with language-model workflows.
• You work independently. You can take an unclear goal, decide what to do first, find what you need, and carry the work through to a result. You bring people in early when a decision, risk, or dependency needs their input.

How we work

AI agents are core to how we get work done. You'll spend a substantial part of your day running multiple agents in parallel, building tools and workflows with them, and checking what they produce. You own the result. Verify the facts, calculations, code, and claims before they go into a decision, a customer conversation, or a release.

When working with clients, take initiative within the agreed goals, scope, access permissions, and review process. Bring options and recommendations, and involve the right people before changing a customer commitment or production workflow.

What success looks like

• Models are chosen against relevant baselines and evaluated on data that reflects their intended use.
• Business users and engineers understand the model's strengths, limits, and operating requirements.
• Deployed workflows are reproducible, observable, and monitored for meaningful changes.
• Agents and custom apps make experimentation more efficient while you remain accountable for the result.