Senior Data Scientist

About the job

We are looking for a Senior Data Scientist with deep experience on the AWS stack to lead end-to-end development of data science and ML solutions—from problem framing and data architecture design to model deployment and monitoring in production. You will partner closely with product, engineering, and business stakeholders to deliver measurable impact using machine learning, experimentation, and advanced analytics on top of AWS data and ML services. Key Responsibilities 1. Problem Definition & Stakeholder Collaboration • Work with business, product, and engineering teams to translate ambiguous problems into clear data science use cases and success metrics. • Define hypotheses, experimentation strategies, and measurable outcomes for ML and analytics initiatives. 2. Data & Feature Engineering (AWS-native) • Design and build robust data pipelines using AWS services such as S3, Glue, Athena, Redshift, EMR, Lambda, Step Functions. • Develop scalable feature stores and reusable data assets for multiple ML use cases. • Ensure data quality, observability, and governance in collaboration with data engineering teams. 3. Modeling & Analytics • Build, train, and optimize models for use cases such as prediction, recommendation, forecasting, personalization, segmentation, anomaly detection, etc. • Use Python and standard ML libraries (e.g., scikit-learn, XGBoost, PyTorch/TensorFlow) for experimentation and prototyping. • Design and run A/B tests, holdout experiments, and causal analyses to measure impact. 4. MLOps & Deployment (AWS SageMaker) • Productionize models using Amazon SageMaker (training, tuning, endpoints, pipelines, model registry). • Implement CI/CD for ML, monitoring and alerting for model drift, data drift, and performance degradation. • Optimize cost and performance of deployed models and pipelines. 5. Leadership & Mentoring • Provide technical leadership on projects, setting standards for experimentation, documentation, and code quality. • Mentor junior data scientists and analysts; contribute to best practices, templates, and internal tooling. • Advocate for data-driven decision-making across the organization.

Requirements

• AWS
• Python
• Data engineering
• Machine learning
• MLOps

Qualifications

• 7+ years of hands-on experience in data science or applied machine learning roles

Preferred Technologies

• AWS
• Python
• Data engineering
• Machine learning
• MLOps