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Ambush

Data Scientist / ML Engineer

Ambush

. Translate planning, operations and sales business questions into modelable problems with clear success metrics .

Posted 10/11/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying machine learning models, with a strong foundation in classical machine learning, statistics, and optimization techniques. Proficient in Python and cloud platforms, with a focus on demand forecasting and operations research.

Highest-signal resume keywords
Machine Learning Model DeploymentDemand ForecastingOptimization Problem SolvingPython ProgrammingCloud Platform Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Machine LearningStatisticsTime Series AnalysisOptimizationAPI DesignSQLData ValidationSimulationRegression AnalysisInventory Management
Tools & Technologies
PythonFastAPIAWSAzureGCPPandasNumpyScikit-learnPyomoOR-Tools
Certifications & Qualifications
MSc in Operations ResearchPhD in Statistics
Industry Keywords
Operations ResearchSupply ChainDemand ForecastingBacktestingDrift Detection

Tech Stack

Tools & technologies
AWSAzureCloudGoogle Cloud PlatformNumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact
  • Translate planning, operations and sales business questions into modelable problems with clear success metrics
  • Build demand forecasting models, including for irregular and sparse demand
  • Formulate and solve optimization problems with LP/MIP solvers
  • Use simulation to test scenarios
  • Deploy, version, monitor and retrain models in production in close collaboration with data engineering
  • Build and maintain APIs and services using Python and FastAPI on AWS, Azure or GCP
  • Follow security best practices
  • Design, build and deploy AI-enhanced applications
  • Take machine learning solutions through to production

Requirements

What you’ll need
  • Solid classical machine learning and statistics knowledge: regression, trees and boosting, regularization, probability and prediction intervals
  • Correct validation, including temporal backtesting without data leakage, and metrics tied to business goals
  • Time series and demand forecasting experience
  • Operations research experience, including formulating optimization problems and solving them with Pyomo, OR-Tools, PuLP or similar
  • Familiarity with Monte Carlo and discrete-event simulation, such as SimPy
  • Conceptual knowledge of inventory and supply chain: safety stock, service level, lead time and turnover
  • Fluent Python for data, including pandas, numpy and scikit-learn
  • Advanced SQL
  • Production-grade backend experience
  • Proven experience taking models to production, including deployment, monitoring, drift detection and retraining
  • Hands-on experience with a major cloud platform
  • Experience with API design and backend security basics
  • MSc or PhD in Operations Research, Industrial Engineering, Statistics or another quantitative field is nice to have
  • Senior-level experience

Benefits

Comp & perks
  • People-first company culture
  • Supportive, trusted and empowering work environment
  • Team collaboration and mutual support
  • Remote work