Data Scientist

Key responsibilities:

AWS Data Science Environment:

Hands-on experience with Sage Maker, Lambda, Step Functions, S3, Athena. Model deployment and pipeline orchestration in AWS.

OCR Use-Case Development:

Proficiency with Amazon Tex-tract, Tesseract, and LLM- based OCR.Building document parsing pipelines, validations, and rule

Python Proficiency:

Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Writing clean, modular, and testable code.

Traditional Machine Learning Models:

Experience with regression (linear, ridge), classification (logistic regression, decision trees, random forests), clustering (k-means, DBSCAN), and time-series forecasting (ARIMA, Prophet). Model evaluation, tuning, and deployment.

Business Requirement Translation:

Ability to convert business problems into data-driven solutions. Designing KPIs, metrics, and actionable insights.

Stakeholder Collaboration:

Effective communication with technical and non-technical stakeholders. Experience in cross-functional team.

Data Engineering Basics:

Familiarity with SQL and big data processing tools (e.g., Amazon Athena).

Model Evaluation & Experimentation:

A/B testing, statistical analysis, and performance metrics.

Security & Compliance Awareness:

Understanding of data privacy, PII handling, and compliance standards (e.g., GDPR).

Agile Methodologies:

Experience working in Agile/Scrum teams using tools like Jira or Azure DevOps.