Data Scientist

Job Description

Functional Area: Data Science & AI/ML Employment Type: Full Time, Permanent Role Category: Engineering and Technology Experience: 4-6 years Job Description: As a growing organization in the healthcare domain, we seek a Data Science Application Expert with expertise in JupyterLab, SAS Studio, and R Studio. The ideal candidate will design, develop, and optimize data science workflows, ensuring robust, scalable, and efficient data processing pipelines. You will collaborate with cross-functional teams to support data-driven decision-making, build machine learning models, and implement analytical solutions aligned with industry standards for security and compliance. Key Responsibilities: Data Science & Application Management: Develop, manage, and optimize JupyterLab, SAS Studio, and R Studio environments for data science workflows. Support and troubleshoot Jupyter Notebooks, R scripts, and SAS programs for performance and reliability. Assist data scientists and analysts in configuring and optimizing their analytical workflows. Maintain and enhance computational environments to support data exploration, statistical modeling, and machine learning. Machine Learning & Analytics: Implement and manage machine learning models using Python (TensorFlow, Scikit-learn, PyTorch), R, and SAS. Automate data preprocessing, feature engineering, and model training workflows. Work with AWS SageMaker, EMR, and other cloud-based ML solutions for scalable model deployment. Monitor and optimize model performance, ensuring reproducibility and efficiency. Data Engineering & Integration: Design and optimize ETL (Extract, Transform, Load) pipelines to handle large-scale healthcare datasets. Integrate JupyterLab, SAS Studio, and R Studio with cloud-based storage (AWS S3, Redshift, Snowflake). Manage structured and unstructured data, ensuring data integrity and compliance with healthcare standards (HIPAA, GDPR). Optimize query performance for large datasets using SQL, Pandas, and SAS data steps. Infrastructure as Code (IaC) & Cloud Deployment: Deploy and manage JupyterHub, RStudio Server, and SAS Viya on AWS, Azure, or on-premise environments. Automate environment provisioning using Terraform, AWS CDK, or CloudFormation. Ensure efficient resource allocation and auto-scaling of cloud-based data science environments. Security & Compliance: Implement role-based access controls (RBAC) for secure access to data science applications. Ensure data security and encryption using AWS KMS, Secrets Manager, and IAM policies. Adhere to HIPAA, GDPR, and other regulatory compliance requirements for healthcare data. Collaboration & Stakeholder Management: Work closely with data scientists, engineers, and business analysts to understand analytical requirements. Participate in Agile ceremonies, including sprint planning, standups, and retrospectives. Provide technical guidance and mentorship to junior team members. Job Requirements: Education: Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, or related fields. Experience: 4-6 years of experience in data science application management, focusing on JupyterLab, SAS Studio, and R Studio. Technical Skills: Core Data Science Platforms: Expertise in JupyterLab, SAS Studio, and R Studio for data science workflows. Strong understanding of SAS programming (Base SAS, Advanced SAS, SAS Viya), R, and Python. Experience in managing and scaling JupyterHub, RStudio Server, and SAS Viya in cloud or on-prem environments. Programming & Frameworks: Proficiency in Python, R, SAS, SQL, and shell scripting. Experience with Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch for machine learning. Cloud & Infrastructure: Experience in deploying and managing JupyterLab, R Studio, and SAS Viya on AWS, Azure, or GCP. Hands-on experience with AWS SageMaker, Glue, Lambda, Step Functions, and EMR. Proficiency in Terraform, AWS CDK, or CloudFormation for infrastructure automation. Database Management & ETL: Experience with SQL, NoSQL databases (PostgreSQL, DynamoDB, Snowflake, Redshift, MongoDB). Hands-on experience in ETL pipelines and data wrangling using SAS, Python, and SQL. DevOps & CI/CD Tools: Familiarity with CI/CD pipelines using Jenkins, GitLab, or AWS native tools. Experience with Docker, Kubernetes, and containerized deployments. Additional Skills: Event-Driven Architecture: Experience in real-time data processing using Kafka, Kinesis, or SNS/SQS. Security Best Practices: Implementation of secure access controls and data encryption. Cost Optimization: Understanding cloud pricing models and optimizing compute resources. Agile Development: Hands-on experience with Agile methodologies like Scrum and Kanban. Key Attributes: Problem-Solving Mindset: Ability to troubleshoot complex data science workflows and propose scalable solutions. Detail-Oriented: Strong focus on data integrity, performance optimization, and reproducibility. Collaborative: Team player who thrives in a dynamic, cross-functional environment. User-Centric Approach: Commitment to delivering scalable and efficient data science applications.