WHAT DOES A DATA SCIENCE INTERN DO?

Published: October 4, 2024 – The Data Science Intern works closely with experienced Data Scientists to analyze business challenges and opportunities through advanced analytics models. This role involves collaboration with team members to facilitate the development of analytical solutions that align with business needs, emphasizing best practices for model reusability and automation. The intern also supports project execution, advocates for data-driven decision-making, and creates impactful presentations to communicate insights and project outcomes effectively.

A Review of Professional Skills and Functions for Data Science Intern

1. Data Science Intern Functions

  • Data Analysis: Running exploratory tests to identify valuable and innovative insights from data.
  • Project Coordination: Support project plans and timelines.
  • Data Lifecycle Management: Contribute to the various phases of development and data lifecycle including design, data modeling, testing, tuning, deployment, and monitoring.
  • Data Science: Research and implement cutting-edge techniques in data science to solve real business problems.
  • Process Improvement: Provide analysis and recommendations on the efficiency and integrity of the current process.
  • Data Cleansing: Assist in cleansing, manipulation, and consolidation of data from multiple sources.
  • Dashboard Development: Develop dynamic dashboards that translate raw data into actionable information for business users.
  • Automation: Study and document manual processes and develop a plan for automating them using robotic process automation software.
  • Statistical Modeling: Apply statistical and machine learning models on supply chain management.
  • Project Management: Potential for managing assigned projects and ensuring projects are complete, accurate, and on time.
  • Team Coordination: Coordinate project plans with other team members under the Intern Manager’s guidance.

2. Data Science Intern Job Description

  • Data Architecture: Map and document the current data architecture.
  • Project Support: Support the definition of the new data architecture and implementation roadmap in collaboration with the IT and software engineering team.
  • Technical Integration: Creation of the technical link between current data sources, cloud services, and BI applications.
  • Data Analytics: Accelerate data analytics for both technical and business data.
  • IoT Integration: Equipment IoT connection to the preferred BI application.
  • Dashboard Development: Take responsibility for IoT dashboard creation.
  • AI/ML Exploration: Investigate AI and machine learning possibilities with currently connected equipment.
  • Data Science: Carry out data science tasks and projects, in particular proofs of concept focused on the integration of new data sources in risk, fraud, and marketing models.
  • Cloud Migration: Support migration to cloud analytics environments and task automation using Python/R and database languages (both SQL and non-SQL).
  • Optimization: Apply optimization methods to scheduling problems and simulate different delivery scenarios.
  • Scheduling System Development: Develop and implement a scheduling system that considers various perspectives on the delivery process to reduce costs and improve customer satisfaction.

3. Data Science Intern Overview

  • Data Analytics: Performs data analytics to support business.
  • Machine Learning: Explores machine learning techniques for their business application.
  • Business Analysis Support: Supports subject matter experts in specific business analysis areas while expanding his/her knowledge base.
  • Project Requirements Gathering: Assists with gathering relevant information and ensuring understanding of the project requirements.
  • Communication: Break down complex concepts into comprehensible concepts to ensure an appropriate level of communication and understanding for the project stakeholders.
  • Reporting: Prepares reports for senior management to provide updates on the projects’ progress, risks, results, and successes.
  • Audience Analysis: Analyzes briefs and identifies the target audience to determine objectives and creative recommendations.
  • Data Science Enhancement: Enhances existing data science solutions with new ML algorithms, feature engineering, and benchmarking.
  • Market Scaling: Scales analytics solutions to new markets and communicates the findings.
  • Solution Design: Understands business needs to design and implement machine learning solutions to automate data collection processes.
  • Collaboration: Collaborates with peer engineering teams and downstream data analysts to continuously and iteratively improve workflows and data storage practices.

4. Data Science Intern Tasks

  • Development Practices: Follow good development practices, innovative frameworks, and technology solutions that help businesses move faster.
  • Team Collaboration: Contribute to brainstorming and help other team members in their projects.
  • Reporting: Prepare written reports or PowerPoint slides in English.
  • Data Science Development: Develop data science code recipes.
  • Framework Building: Build a framework that teams could maximize to accelerate recipe development.
  • Performance Benchmarking: Develop the performance benchmarks.
  • Data Analysis: Conduct exploratory data analysis from complex, disparate data sources.
  • Statistical Modeling: Design, develop, and validate statistical models for novel medical applications.
  • Algorithm Evaluation: Implement and evaluate algorithms based on research literature.
  • Code Maintenance: Create, document, and maintain code.
  • Reporting: Report findings to internal teams.

5. Data Science Intern Details and Accountabilities

  • Data Science Collaboration: Work under the supervision of other Data Scientists to understand and help translate business issues and opportunities using advanced analytics models.
  • Data Exploration: Use advanced data exploration, statistical understanding, and data knowledge to identify and deliver business insights in partnership with Product Managers and other Data Scientists.
  • Analytical Solutions: Implement feasible analytical solutions to business requirements.
  • Model Development: Develop analytical models following best practices for reusability, stability, and automation.
  • Documentation: Ensure that their models’ assumptions are well-documented.
  • Project Support: Support the execution of data science projects.
  • Data Advocacy: Advocate for data-centric decision-making.
  • Python Programming: Compile and test necessary Python programming scripts to finalize the data science product.
  • Sales Support: Support the sales team by providing data and insights.
  • Executive Presentations: Compile and make executive presentations, storytelling of the project, innovation, and impact.