Hybrid Data Scientist

at SiteMinder

• Job title: Data Scientist
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Cloud, PySpark, Python, PyTorch, Scikit-Learn, SQL, Tensorflow
• Location requirements: Hybrid, Pune, India

About the role

• Data Scientist at SiteMinder building machine learning solutions. Collaborating with teams to integrate models and tackle complex data science challenges.

Responsibilities

• Design and develop end-to-end ML solutions — from data exploration and feature engineering to model training, validation, and deployment.
• Collaborate cross-functionally with engineers, analysts, and product teams to integrate predictive and recommendation models into customer-facing and internal applications.
• Implement scalable ML pipelines using Databricks, PySpark, and Delta Lake, ensuring reproducibility, performance, and maintainability.
• Run controlled experiments (A/B tests, uplift modelling, causal inference) to measure model performance and quantify business impact.
• Operationalise models through CI/CD and MLOps best practices, including model versioning, monitoring, retraining strategies, and governance.
• Monitor production systems for drift, performance degradation, and anomalies, applying explainability and fairness techniques where needed.
• Contribute to the development of feature stores and reusable data assets to accelerate experimentation and deployment cycles.
• Stay current with emerging trends in ML, MLOps, and cloud data technologies to continuously improve model accuracy, scalability, and efficiency.

Requirements

• 5+ years of 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 (linear/logistic regression, tree-based methods, ensemble learning) to modern deep learning architectures (CNNs, RNNs, transformers, graph neural networks, diffusion and foundation models).
• 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).
• Familiarity with retrieval-augmented generation (RAG) and fine-tuning of large language models is a plus.
• Proficiency in SQL and distributed data frameworks, with experience in feature engineering at scale.

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 celebrations
• Employee Resource Groups (ERG) to help you connect and get involved
• Investment in your personal growth offering training for your advancement