• Own and scale a modern healthcare data platform — building core services, automation, and observability that power real-world patient and provider outcomes • Architect and build core services, automation tools, and integrations that power our client's data ecosystem • Own high-impact platform components, improve pipeline reliability and observability • Partner closely with data engineering, analytics, and DevOps to advance the scalability and developer experience of our client's data platform

• Design, build, and operate scalable, reliable data pipelines and data infrastructure • Ensure high-quality data is accessible, trusted, and ready for analytics and data science • Build and maintain data pipelines for ingestion, transformation, and export across multiple sources and destinations • Develop and evolve scalable data architecture to meet business and performance requirements • Partner with analysts and data scientists to deliver curated, analysis-ready datasets and enable self-service analytics • Implement best practices for data quality, testing, monitoring, lineage, and reliability • Optimize workflows for performance, cost, and scalability (e.g., tuning Spark jobs, query optimization, partitioning strategies) • Ensure secure data handling and compliance with relevant data protection standards and internal policies • Contribute to documentation, standards, and continuous improvement of data platform and engineering processes • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices • Build and maintain MLOps automation: CI/CD for ML, environment management, artifact handling, versioning of data/models/code

Role Description As a Data Science Intern, you will collaborate with our data science and analytics teams to work on meaningful projects involving data analysis, predictive modeling, and statistical modeling. You will have the opportunity to apply your academic knowledge in a practical, fast-paced environment, contribute to key data-driven projects, and gain valuable experience with industry-leading tools and technologies. - Assist in collecting, cleaning, and preprocessing data from various sources. - Perform exploratory data analysis to identify trends, patterns, and anomalies. - Develop and implement machine learning models and algorithms. - Create data visualizations and reports to communicate findings to stakeholders. - Collaborate with team members on data-driven projects and research. - Participate in meetings and contribute to discussions on project progress and strategy. - Work with large datasets to clean, preprocess, and analyze data. - Build and deploy statistical and machine learning models to generate actionable insights. - Conduct exploratory data analysis (EDA) to uncover trends, patterns, and correlations. - Assist in the creation of data visualizations and dashboards for reporting insights. - Support the development and improvement of data pipelines and algorithms. - Collaborate with cross-functional teams to understand data needs and translate them into actionable analytics solutions. - Contribute to the documentation and presentation of results, findings, and recommendations. - Participate in team meetings, brainstorming sessions, and project discussions. Qualifications - Any Graduate / PassOuts / Freshers can apply. - Currently pursuing a Bachelor's or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field. - Proficiency in programming languages such as Python, R, or SQL. - Strong foundation in statistics, probability, and data analysis techniques. Requirements - Duration: 2 Months (with the possibility of extending up to 6 months). - MODE: Work From Home (Online). Benefits - Internship Certificate. - Letter of recommendation. - Stipend Performance Based. - Part time work from home (2-3 Hrs per day). - 5 days a week, Fully Flexible Shift.

• Design and Build Data Pipelines: Construct and maintain robust, scalable data pipelines to seamlessly integrate data from multiple sources • Data Acquisition and Storage: Develop efficient database solutions to collect, store, and secure data, ensuring accessibility and protection • Data Transformation: Create algorithms to convert raw data into actionable insights, aligning with our business objectives • Pipeline Optimization: Continuously evaluate and enhance existing data systems and processes for improved efficiency and reliability • Quality Assurance: Implement systems to validate and ensure the accuracy and consistency of data sets • Collaboration: Work closely with machine learning engineers, data scientists, analysts, and other stakeholders to meet the organization's data-centric needs