Data Scientist (AI/ML)

TL;DR

Senior Data Scientist (AI/ML): Building and scaling machine learning solutions that drive product intelligence and data-informed decision-making for a hotel commerce platform with an accent on model development, validation, and productionisation. Focus on integrating models into products, tackling complex data science challenges related to prediction, recommendation, and optimisation, and implementing scalable ML pipelines.

Location: Hybrid in Pune

Company

hirify.global is the world's leading open hotel commerce platform, supporting 50,000 hotels in 150+ countries.

What you will do

• Design and develop end-to-end ML solutions from data exploration and feature engineering to model deployment.
• Collaborate cross-functionally with engineers, analysts, and product teams to integrate predictive and recommendation models.
• Implement scalable ML pipelines using Databricks, PySpark, and Delta Lake.
• Run controlled experiments (A/B tests, uplift modelling, causal inference) to measure model performance and business impact.
• Operationalise models through CI/CD and MLOps best practices, including model versioning and monitoring.
• Contribute to the development of feature stores and stay current with emerging ML and MLOps trends.

Requirements

• Extensive hands-on experience applying machine learning and statistical modelling in production or product-oriented environments.
• Proven understanding of the full spectrum of ML techniques, from traditional models to modern deep learning architectures.
• Strong experience in Python, with proficiency in Scikit-learn, Autogluone, PyTorch or TensorFlow, and PySpark MLlib.
• Demonstrated ability to design scalable ML pipelines and automate workflows with MLOps tools (MLflow, Kubeflow, Databricks ML runtime, AWS Sagemaker, or AWS Bedrock).
• Proficiency in SQL and distributed data frameworks, with experience in feature engineering at scale.

Nice to have

• Familiarity with real-time ML applications, such as online learning or streaming inference.
• Exposure to forecasting, anomaly detection, or probabilistic modelling in production systems.
• Experience contributing to open-source projects, writing technical blogs, or presenting at data science conferences.
• Interest in continuous learning and keeping up with cutting-edge AI research.

Culture & Benefits

• Mental health and well-being initiatives.
• Generous parental (including secondary) leave policy.
• Flexibility to work in a Hybrid model (2-3 days in-office).
• Paid birthday, study and volunteering leave every year.
• Sponsored social clubs, team events, and Employee Resource Groups (ERG).
• Investment in personal growth offering training for advancement.