Senior Data Scientist – AWS

About the job

Job Title: Senior Data Scientist – AWS Location: Remote / Hybrid Department: Data & AI Employment Type: Full-time About the Role We are seeking an experienced Senior Data Scientist to lead end-to-end development of data science and ML solutions. The ideal candidate will have deep experience on the AWS stack, a strong proficiency in Python and ML/data libraries, and a solid understanding of statistics and experimental design. Key Responsibilities: • 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. • 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. 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. 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. 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. Required Qualifications: • 7+ years of hands-on experience in data science or applied machine learning roles. • Strong proficiency in Python and ML/data libraries (pandas, numpy, scikit-learn, XGBoost, PyTorch/TensorFlow). • Demonstrated experience building and deploying ML solutions on AWS, including: • Data: S3, Glue, Athena, Redshift, EMR / AWS Lake Formation • ML: SageMaker (training jobs, endpoints, pipelines, model registry) • Orchestration/Integration: Lambda, Step Functions, EventBridge, API Gateway • Solid understanding of statistics, experimental design, and causal inference (A/B testing, hypothesis testing, confidence intervals, etc.). • Proven track record of delivering ML solutions into production with measurable business impact. • Strong SQL skills and comfort working with large-scale datasets in data lake/data warehouse environments. • Excellent communication skills—able to explain complex topics to both technical and non-technical stakeholders.

Requirements

• AWS
• Python
• ML Data Libraries
• Data Engineering
• MLOps

Preferred Technologies

• AWS
• Python
• ML Data Libraries
• Data Engineering
• MLOps