Senior Machine Learning Engineer

HRM Group

Senior Machine Learning Engineer focused on promotions optimization and sales forecasting. Working with AWS and advanced predictive and analytical models for global decision-making.

Posted 7/3/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftAWSCloudNumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

The role involves a strategic project focused on promotions optimization and sales forecasting across international markets.
Responsibilities include designing, developing, and operationalizing advanced predictive and analytical models to support commercial decision-making at a global scale.
The consultant will act as a senior hands-on expert, combining strong data science expertise with robust engineering skills to deliver production-ready solutions on AWS.
The role will contribute to demand forecasting, promotional effectiveness analysis, price elasticity modeling, and multi-market modeling, taking into account seasonality, market dynamics, and external demand drivers.
The position also includes implementing MLOps practices on Amazon SageMaker, model monitoring, automated retraining, documentation, and knowledge transfer, while collaborating with data teams and business stakeholders across multiple regions.

Requirements

What you’ll need

Strong experience in forecasting models and/or promotions analytics, preferably within Retail, CPG, or e-commerce contexts.
Experience designing and maintaining demand and sales forecasting models at different levels of granularity (SKU, category, channel, and market).
Strong knowledge of classical forecasting models such as ARIMA, SARIMA, SARIMAX, ETS, Holt–Winters, and state-space models.
Experience with modern forecasting libraries such as Prophet, NeuralProphet, GluonTS, Statsforecast, Nixtla / MLForecast, and DeepAR.
Experience with XGBoost, LightGBM, and CatBoost for tabular forecasting use cases.
Knowledge of hierarchical forecasting and reconciliation methods such as MinT, bottom-up, and top-down approaches.
Proven experience modeling price elasticity, promotional uplift, and causal impact.
Familiarity with approaches such as CausalImpact, Difference-in-Differences, uplift modeling, and Bayesian structural time series.
Strong capability to handle seasonality, intermittent demand, and multi-market heterogeneity.
Strong fluency with AWS, especially Amazon SageMaker and SageMaker components.
Experience implementing MLOps practices, including automated retraining, monitoring, and drift detection.
Solid experience with Redshift or equivalent cloud data warehouses.
Production-level experience with Python, including pandas, NumPy, scikit-learn, PyTorch, or TensorFlow.
Ability to design and evaluate A/B tests and backtesting frameworks to validate model performance and business impact.
Excellent command of English (C1 level).

Benefits

Comp & perks

Hybrid working & flexibility: Work from home with flexibility and comfort.
30 days off: Generous time off—30 days per year.
Training & tech library: Budget for tailored project training and access to resources to stay current with the latest tech stacks.
Health & welfare: Contractual health fund extended to family and a welfare budget tailored to your needs.
Referral bonus: Know a great candidate? Refer them and receive a financial reward with no referral limits.
Additional perks: Electronic meal vouchers and exclusive discounts on tech, travel, and lifestyle.

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

ARIMASARIMASARIMAXETSHolt–WintersXGBoostLightGBMCatBoostPythonStatistical Modeling

Soft Skills

CollaborationCommunication