Senior Applied Scientist/Machine Learning Engineer for Decision-Making Platform Development

7 days ago

Bengaluru, Karnataka, India beBeeMachineLearning Full time US$ 90,000 - US$ 1,20,000

We are seeking a highly skilled Data Analyst/Machine Learning Engineer to enhance and scale our existing systems.

  • This individual will play a key role in developing new capabilities that support our decision-making platform.

Key Responsibilities:

  • BUILD, TRAIN, AND DEPLOY MACHINE LEARNING MODELS FOR FORECASTING, PRICING, AND OPTIMIZATION
  • APPLY ADVANCED TECHNIQUES LIKE CAUSAL INFERENCE, COUNTERFACTUAL ANALYSIS, AND REINFORCEMENT LEARNING TO IMPROVE DECISION-MAKING UNDER UNCERTAINTY
  • WORK WITH LARGE-SCALE, NOISY, AND TEMPORALLY COMPLEX DATASETS
  • COLLABORATE CROSS-FUNCTIONALLY WITH ENGINEERING AND PRODUCT TEAMS TO MOVE MODELS FROM RESEARCH TO PRODUCTION
  • DESIGN OFFLINE EVALUATION FRAMEWORKS AND SIMULATIONS TO VALIDATE NEW ALGORITHMS BEFORE LIVE ROLL-OUT
  • GENERATE INTERPRETABLE AND TRUSTED OUTPUTS TO SUPPORT ADOPTION OF AI-DRIVEN RATE RECOMMENDATIONS
  • CONTRIBUTE TO THE DEVELOPMENT OF AN AI-FIRST PLATFORM THAT REDEFINES HOSPITALITY REVENUE MANAGEMENT

Requirements:

  • Bachelor's or Master's degree in Computer Science or related field.
  • 5–10 years of hands-on experience in a product-centric company, ideally with full model lifecycle exposure.
  • Demonstrated ability to apply machine learning to solve real-world business problems.
  • Proficient in Python and machine learning libraries such as scikit-learn, PyTorch, and XGBoost.
  • Strong knowledge of forecasting models (time-series and ML-based).
  • Deep understanding of machine learning and deep learning foundations.
  • Comfort with optimization under uncertainty and experience evaluating ML model performance rigorously
  • Ability to work independently and manage projects end-to-end.

Preferred Experience:

  • Experience in revenue management, pricing systems, or demand forecasting, particularly within the hotel and hospitality domain.
  • Applied knowledge of reinforcement learning techniques (e.g., bandits, Q-learning, model-based control).
  • Familiarity with causal inference methods (e.g., DAGs, treatment effect estimation).
  • Strong written and verbal communication skills to explain complex technical concepts clearly to cross-functional teams.
  • Proven experience in collaborative product development environments, working closely with engineering and product teams.

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