Data Scientist / ML Engineer

Job not on LinkedIn

🕒 October 11

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Ambush

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2015

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Ambush is a People Company that specializes in identifying, retaining, and integrating highly-talented remote designers and engineers into teams across various sectors. With almost a decade of experience, Ambush focuses on building long-term, dedicated partnerships, providing clients with exceptional software development and design services. The company prides itself on its human-centered approach, remote team integration, and commitment to quality, making it a trusted partner for organizations in the financial, medical, and privacy sectors.

📋 Description

• 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

• 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

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