HostBooks - Senior Data Scientist - Machine Learning (5-10 yrs)

About the Role :

We are seeking a highly skilled and hands-on Senior Data Scientist with 7+ years of experience to lead the design and implementation of robust machine learning pipelines and drive data-driven decision making across the organization. This role requires a strategic thinker who can bridge the gap between complex data science concepts and practical business solutions, while ensuring model integrity, explainability, and compliance in production environments.

As a Senior Data Scientist, you will have end-to-end ownership of the model life cycle, from data ingestion and feature engineering to model deployment, monitoring, and governance. Youll work closely with AI engineers, product teams, and stakeholders to deliver high-impact solutions that drive business value.

Key Responsibilities :

Machine Learning & Predictive Modeling :

- Design and build sophisticated predictive models using Scikit-learn, XG Boost, LightGBM, and CatBoost for various use cases.

- Develop advanced forecasting models using Prophet, ARIMA, and neural forecasting techniques for time series analysis.

- Implement anomaly detection systems and risk scoring models for fraud detection and security applications

- Create recommendation systems and personalization algorithms using collaborative filtering and deep learning approaches

AI Integration & Pipeline Development :

- Collaborate with AI engineers to integrate traditional ML components into LangChain and LLM-driven intelligent systems

- Design hybrid architectures that combine classical ML with generative AI for enhanced business solutions

- Develop evaluation frameworks for comparing traditional ML and LLM based approaches

- Implement retrieval systems that enhance LLM performance with domain specific knowledge

Model Lifecycle Management :

- Automate comprehensive model lifecycle processes including training, validation, deployment, and rollback procedures

- Implement continuous training pipelines using MLFlow, Kubeflow, and Weights & Biases

- Design and maintain model monitoring systems for drift detection, performance degradation, and data quality issues

- Establish model governance frameworks ensuring reproducibility and auditability

Data Validation & Quality Assurance :

- Lead the development of pre-model and post-model validation frameworks using DeepChecks, Great Expectations, and custom validation rules

- Implement fairness and bias detection systems using Fairlearn and custom algorithmic auditing tools

- Design comprehensive data quality monitoring and alerting systems

- Conduct statistical testing and hypothesis validation for model performance claims

Compliance & Security :

- Ensure PII protection and DPDP compliance through secure data preprocessing and anonymization techniques

- Implement synthetic data generation pipelines using Gretel.ai and other privacy-preserving technologies

- Design policy-driven access controls and data governance frameworks using Apache Griffin and DataHub

- Conduct privacy impact assessments and implement differential privacy techniques where applicable

Model Explainability & Auditing :

- Develop comprehensive model explainability frameworks using SHAP, LIME, and custom interpretation tools

- Conduct reasoning-based walkthroughs and accuracy audits for deployed models

- Perform bias analysis and fairness assessments across different demographic groups

- Design and implement A/B testing frameworks for model performance evaluation

Data Engineering & Pipeline Architecture :

- Design and implement scalable ETL/ELT pipelines using Apache Spark, Flink, and modern data processing frameworks

- Leverage Redis for intelligent caching strategies and real-time feature serving

- Implement streaming data processing using Apache Kafka, RabbitMQ, and event-driven architectures

- Optimize data pipeline performance and ensure data consistency across distributed systems