Top 5 Technical Skills:
- Statistical modeling, machine learning, AI, and applied analytics
- Python
- AWS ML Platforms (AWS SageMaker, MLFlow, S3, compute services, Redshift)
- Model deployment and MLOps practices
- Data Processing
Job Description:
We are seeking a Full Stack Data Scientist to develop AI/ML solutions end-to-end, from business problem formulation and model development through production-ready application delivery and operationalization. This role combines deep modeling expertise, strong software engineering skills, and practical MLOps experience. The ideal candidate builds models that matter, writes code that lasts, and partners with platform teams to deploy, monitor, and operate AI/ML solutions efficiently and reliably at scale.
Key Responsibilities • Translate complex business requirements into AI/ML-based technical solutions and ensure efficiency, scalability and reliability • Design, develop, validate, and document AI/ML models and applications • Build production-grade Python code and pipelines for data processing, feature engineering, training, and inference. • Develop model-driven applications and services (batch or real-time). • Apply software engineering best practices including modular design, testing, code reviews, and CI/CD. • Collaborate with MLOps teams on deployment, monitoring, versioning, and retraining. • Implement model performance, stability, and data drift monitoring. • Produce documentation to support governance, validation, and audit requirements.
Required Qualifications • Proven hands-on experience (6+ years preferred) in production-ready models and applications that solve real business problems while actively participating in MLOps to ensure solutions operate reliably in production. • Strong experience in statistical modeling, machine learning, AI, and applied analytics. • Advanced proficiency in Python, ML libraries, SQL, and big data processing (e.g. pandas, NumPy, scikit-learn, TensorFlow, PySpark ). • Experience writing production-ready, maintainable code and application design. • Strong experience with AWS cloud ML platforms (e.g., AWS SageMaker, MLFlow, S3, compute services, Redshift). • Experience with model deployment and MLOps practices • Strong problem-solving and communication skills.
Education Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field.
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