Senior Data Scientist, Forecasting & Optimization

Enlighten AI Labs; Atlanta (GA); On-site; USD 140,000 - 210,000; Full time

Job summary

Enlighten AI Labs, Atlanta, GA, is seeking a Senior Data Scientist to build forecasts and decision tools for demand, inventory, capacity, and staffing. You will guide AI agents to explore models, set success criteria, and verify forecasts under real constraints.

You will collaborate with engineers and customer teams to deploy forecasts, monitor performance, and translate operational needs into robust, actionable models. Strong Python/SQL and time-series expertise are essential.

Qualifications

• Track record delivering forecasting, optimization, or planning models used in a business process.
• Strong Python and SQL skills with time-series validation and uncertainty estimation.
• Ability to choose sensible baselines, detect leakage, and evaluate performance across segments and horizons.
• Experience translating operational constraints into models or decision rules people can understand and use.
• Hands-on experience running multiple AI agents and building custom analytical apps while verifying code, assumptions, and results.
• Clear communication about tradeoffs, limitations, and what would change a recommendation.
• Initiative to investigate data and process problems and carry an agreed solution through delivery.
• Experience with demand planning, supply chains, inventory, staffing, or restaurant operations.
• Experience with hierarchical forecasting, constrained optimization, or simulation.
• Experience monitoring deployed models and working with engineers on production 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.

Responsibilities

• Set the planning horizon, operating constraints, and success criteria with the people who will use the forecast.
• Prepare time series and operational data. Check calendar alignment, hierarchy, leakage, missing data, and changes in business definitions.
• Compare simple baselines with more advanced models using backtests that reflect how teams will actually use the forecast.
• Quantify uncertainty and test how different assumptions and errors affect operating decisions.
• Run multiple AI agents in parallel every day. Direct them to write modeling code, run backtests, and build custom apps for scenario planning.
• Independently verify the code, results, and operating assumptions before teams use them.
• Work with engineers and customer teams to put forecasts into regular use and monitor performance as actuals come in.

Skills

Forecasting

Optimization

Python

SQL

Time-series validation

Uncertainty estimation

Communication

Independent work

Tools

Python tooling

SQL databases

Job description

Senior Data Scientist, Forecasting & Optimization

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 forecasts and decision tools for teams planning demand, inventory, capacity, and staffing. You’ll direct AI agents to explore candidate models and scenarios, set the criteria they must meet, and verify how forecasts hold up under real operating constraints.

What you'll do

• Set the planning horizon, operating constraints, and success criteria with the people who will use the forecast.
• Prepare time series and operational data. Check calendar alignment, hierarchy, leakage, missing data, and changes in business definitions.
• Compare simple baselines with more advanced models using backtests that reflect how teams will actually use the forecast.
• Quantify uncertainty and test how different assumptions and errors affect operating decisions.
• Run multiple AI agents in parallel every day. Direct them to write modeling code, run backtests, and build custom apps for scenario planning. Independently verify the code, results, and operating assumptions before teams use them.
• Work with engineers and customer teams to put forecasts into regular use and monitor performance as actuals come in.

What you bring

• A track record delivering forecasting, optimization, or planning models used in a business process.
• Strong Python and SQL skills and practical knowledge of time-series validation and uncertainty estimation.
• The ability to choose sensible baselines, detect leakage, and evaluate performance across relevant segments and horizons.
• Experience translating operational constraints into models or decision rules people can understand and use.
• Hands-on experience running multiple AI agents and building custom analytical apps while verifying code, assumptions, and results.
• Clear communication about tradeoffs, limitations, and what would change a recommendation.
• The initiative to investigate data and process problems and carry an agreed solution through delivery.
• Experience with demand planning, supply chains, inventory, staffing, or restaurant operations.
• Experience with hierarchical forecasting, constrained optimization, or simulation.
• Experience monitoring deployed models and working with engineers on production 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

• Forecasts are evaluated against honest baselines with backtests that match their intended use.
• Stakeholders can understand uncertainty and compare feasible operating choices.
• Recurring forecasts can be rerun, monitored, and reconciled with new actuals.
• Your agents and custom apps shorten investigation and iteration while preserving reliable evaluation.