Data Scientist (Bedrock and Sagemaker, Python, Pandas)

Role: Data Scientist Location: Milton Keynes, UK Hybrid (Inside Ir35) Contract position Key responsibilities: key Skills: Bedrock and Sagemaker, Python, Pandas 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 (eg, 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 (eg, GDPR). Agile Methodologies: Experience working in Agile/Scrum teams using tools like Jira or Azure DevOps. Additional remarks

• Working experience LLMs under Bedrock and Sagemaker both Amazon
• Python with APIs to ChatGPT
• Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers.
  
  key Skills: Bedrock and Sagemaker, Python, Pandas
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 (eg, 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 (eg, GDPR). Agile Methodologies: Experience working in Agile/Scrum teams using tools like Jira or Azure DevOps. Additional remarks
• Working experience LLMs under Bedrock and Sagemaker both Amazon
• Python with APIs to ChatGPT
• Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers.