Applied Scientist, AI Economics (TokenOps, FinOps)

Microsoft•,

5d•$101,800 - $207,600•Remote

About The Position

The Frontier Transformation Framework helps customers become Frontier Firms: organizations where AI and agents operate as part of the business. TokenOps makes the economics of those systems measurable and governable. Within the Frontier Transformation team, this role provides the quantitative planning capability that turns telemetry and uncertainty into defensible investment and operating decisions. We are seeking an Applied Scientist, AI Economics (TokenOps, FinOps) to own the proactive planner, forecasting, optimization, and calibration that make AI economics defensible. You will develop predictive models for cost and delivery distributions, design optimization methods that account for risk and constraints, and calibrate those models against production evidence. This role is for a scientist who can connect rigorous methods with practical customer decisions. You will move between Python implementation, probabilistic forecasting, stochastic optimization, causal inference, and executive explanation; partner with the Telemetry & TokenOps Engineer on the evidence foundation; and turn the work into a reusable forecast and calibration assessment.

Requirements

Master's Degree in Computer Science, Engineering, Data Science or related field AND 4+ years experience applying machine learning, forecasting, or optimization in production OR Bachelor's Degree in Computer Science, Engineering, Data Science or related field AND 6+ years experience applying machine learning, forecasting, or optimization in production OR equivalent experience.

Nice To Haves

Demonstrated ownership of calibrated decision models operating under uncertainty and tied to real production decisions.
Strong Python and scientific machine-learning skills, including disciplined implementation, testing, and reproducibility.
Deep experience with probabilistic forecasting and evaluation of predictive distributions.
Experience formulating and solving stochastic optimization problems with operational or economic constraints.
Working knowledge of CVaR, chance constraints, and other methods for modeling tail risk and decision confidence.
Experience with conformal prediction, empirical calibration, or comparable uncertainty-quantification techniques.
Strong grounding in causal inference and the design or analysis of experiments and observational studies.
Experience deploying and monitoring predictive models through production MLOps practices.
Published or production work in AI economics, FinOps, TokenOps, or decision science.
Experience building Monte Carlo simulations, scenario models, and sensitivity analyses for executive or operational decisions.
Understanding of model, agent, token, infrastructure, and delivery cost drivers in enterprise AI systems.
Experience working with telemetry, attribution, and fact-store data in partnership with data or platform engineers.
Familiarity with model evaluation, drift detection, data-quality controls, and lineage for regulated or high-stakes decisions.
Customer-facing experience translating quantitative results into clear recommendations, tradeoffs, and limitations.
Ability to collaborate with solution architects, engineers, finance stakeholders, and delivery leaders.
A record of turning bespoke analytical work into reusable methods, software, assessments, or intellectual property.

Responsibilities

Own proactive-planner algorithms and predictive-model calibration for TokenOps and FinOps decisions.
Develop probabilistic models that forecast cost, token demand, latency, quality, and delivery outcomes as distributions rather than point estimates.
Design stochastic optimization methods that recommend model, agent, routing, and capacity choices under uncertainty.
Apply CVaR, chance constraints, and related risk measures to keep recommendations within customer cost, reliability, and delivery tolerances.
Build conformal and empirical calibration methods that quantify uncertainty and show when predictive confidence is no longer reliable.
Use causal inference and experimental evidence to distinguish the impact of TokenOps interventions from correlation or external effects.
Develop Monte Carlo simulations and scenario analyses that make tradeoffs, tail risks, and sensitivity visible to decision makers.
Implement production-quality scientific software in Python and establish monitoring, validation, versioning, and MLOps practices for deployed models.
Partner with the Telemetry & TokenOps Engineer to define the fact-store data, attribution, quality, and lineage needed for forecasting and calibration.
Translate model outputs into clear customer recommendations, assumptions, constraints, and decision thresholds.
Create a reusable forecast and calibration assessment that can be applied consistently across customer engagements.