AWS SageMaker Analyst - Remote

AWS SageMaker Analyst - Remote

Posted 1 day ago by 1753968027

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Negotiable
Outside
Remote
USA
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Algorithms Amazon Web Services (AWS) AWS Lambda AWS SageMaker Cloud Computing Cloud Technology Computer Science Continuous Integration and Continuous Delivery Customer Retention Data Engineering Data Ethics Data Governance Data Lakes Data Science Data Visualisation DevOps Financial Services Fraud Prevention And Detection General Insurance Generic Programming Git (Version Control System) Key Performance Indicators (KPIs) Machine Learning Model Building NumPy Pandas (Python Package) Plotly Power BI Probability And Statistics Python (Programming Language) PyTorch (Machine Learning Library) Scikit-learn (Machine Learning Library) Software Development SQL (Programming Language) Stakeholder Management Statistics Tableau (Business Intelligence Software) TensorFlow Transparency (Human-Computer Interaction) Underwriting Unstructured Data Version Control Workflows Xgboost

Summary: We are seeking an AWS SageMaker Analyst to join our Data Science & Advanced Analytics team, focusing on designing, deploying, and maintaining machine learning models on AWS SageMaker. The role involves collaboration with various stakeholders to derive predictive insights that enhance underwriting, claims, customer retention, and product innovation. The ideal candidate should possess a strong background in data science and machine learning, particularly within the financial services or insurance sectors. This position is remote, allowing flexibility in work arrangements.

Key Responsibilities:

  • Design and deploy scalable ML models using Amazon SageMaker (built-in algorithms or custom models).
  • Collaborate with business teams to gather requirements and translate them into machine learning use cases.
  • Analyse structured and unstructured data using tools such as Pandas, NumPy, and SQL.
  • Develop, test, and optimize ML pipelines and workflows using SageMaker Pipelines and Feature Store.
  • Implement MLOps best practices including model versioning, monitoring, and automated retraining.
  • Visualize model performance and outcomes for both technical and non-technical audiences.
  • Ensure compliance with data governance, privacy, and model explainability standards relevant to the insurance industry.
  • Partner with DevOps and data engineering teams to manage integration with cloud data lakes (e.g., S3, Redshift).

Key Skills:

  • Bachelor's or master's degree in computer science, Data Science, Statistics, or related field.
  • 2-5 years of experience in data science or machine learning, preferably in financial services or insurance.
  • Hands-on experience with AWS SageMaker, including model training, hyperparameter tuning, and endpoint deployment.
  • Strong programming skills in Python and experience with ML libraries like Scikit-learn, XGBoost, PyTorch, or TensorFlow.
  • Experience with SQL, AWS services (e.g., S3, Lambda, Step Functions), and Git version control.
  • Working knowledge of MLOps tools and practices (e.g., CI/CD pipelines for ML, SageMaker Model Monitor).
  • Familiarity with data visualization tools such as Tableau, Power BI, or Plotly is a plus.
  • Knowledge of regulatory considerations around data ethics, fairness, and transparency in model building.

Salary (Rate): undetermined

City: undetermined

Country: USA

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

Hi Team,

Please share the below details.

Job Role : AWS SageMaker Analyst

Location : Remote

Hire Type- 12+ Months Contract

Requirement:

Job Title: SageMaker Analyst
Location:
Role Summary:
We are looking for a SageMaker Analyst to join our Data Science & Advanced Analytics team. The ideal candidate will be responsible for designing, deploying, and maintaining machine learning models on AWS SageMaker. You will work closely with data engineers, actuaries, and business stakeholders to unlock predictive insights that support underwriting, claims, customer retention, and product innovation.
Key Responsibilities:
Design and deploy scalable ML models using Amazon SageMaker (built-in algorithms or custom models).
Collaborate with business teams to gather requirements and translate them into machine learning use cases.
Analyse structured and unstructured data using tools such as Pandas, NumPy, and SQL.
Develop, test, and optimize ML pipelines and workflows using SageMaker Pipelines and Feature Store.
Implement MLOps best practices including model versioning, monitoring, and automated retraining.
Visualize model performance and outcomes for both technical and non-technical audiences.
Ensure compliance with data governance, privacy, and model explainability standards relevant to the insurance industry.
Partner with DevOps and data engineering teams to manage integration with cloud data lakes (e.g., S3, Redshift).
Qualifications:
Bachelor's or master's degree in computer science, Data Science, Statistics, or related field.
2 5 years of experience in data science or machine learning, preferably in financial services or insurance.
Hands-on experience with AWS SageMaker, including model training, hyperparameter tuning, and endpoint deployment.
Strong programming skills in Python and experience with ML libraries like Scikit-learn, XGBoost, PyTorch, or TensorFlow.
Experience with SQL, AWS services (e.g., S3, Lambda, Step Functions), and Git version control.
Working knowledge of MLOps tools and practices (e.g., CI/CD pipelines for ML, SageMaker Model Monitor).
Familiarity with data visualization tools such as Tableau, Power BI, or Plotly is a plus.
Knowledge of regulatory considerations around data ethics, fairness, and transparency in model building.
Preferred Skills:
Experience in actuarial, underwriting, or fraud detection models.
Familiarity with cloud cost optimization for ML workloads.
Understanding of insurance KPIs and customer lifecycle analytics.

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