Data Scientist/Engineer Intern

Core Responsibilities

• Data Collection & Preparation: Gather, preprocess, and clean structured and unstructured data to support machine learning workflows and downstream analytics.

• Data Pipeline Development: Design, build, and maintain automated ingestion workflows that integrate cleanly with CI/CD processes.

• Pipeline Monitoring & Optimization: Monitor, troubleshoot, and tune data pipelines to ensure reliable data flow, strong performance, and consistent data quality.

• Data Validation & Quality Assurance: Verify data accuracy and consistency across source systems, ETL pipelines, and dashboards through structured testing and QA practices.

• Data Governance & Compliance: Uphold data governance policies, privacy regulations, and organizational standards across the full data lifecycle.

• Machine Learning Model Evaluation: Train and evaluate machine learning models, identifying weaknesses and surfacing clear opportunities to improve performance.

• Business Intelligence & Reporting: Partner with stakeholders to translate business questions into data requirements and deliver reporting that drives actionable insights.

• Guideline Adherence: Learn, follow, and adapt to evolving annotation protocols, applying them precisely and consistently as project requirements change.

• Annotation Tool Proficiency: Develop expertise in the specialized platforms used for annotation to complete tasks efficiently and to a high standard.

•Cross-Functional Communication: Collaborate with teammates, QA specialists, and project managers to clarify guidelines, flag data issues, and share feedback that strengthens the annotation process.

• Delivery & Deadline Management: Manage workload to complete large batches of work on time without compromising accuracy.

Skills and Qualifications

• Educational Qualifications: Open to fresh graduates and diploma holders in computer science, data science, statistics, engineering, or a related field.

• Programming Proficiency: Working knowledge of Python or a comparable language, with the ability to write functional code for data tasks.

• English Fluency: Strong written and spoken English for clear communication with team members and accurate interpretation of guidelines.

• Attention to Detail: Capacity to sustain focus on repetitive work while maintaining a high bar for accuracy and consistency.

• Comprehension & Protocol Discipline: Strong reading comprehension and the discipline to follow complex, detailed annotation guidelines exactly as specified.

• Independence & Reliability: Ability to work autonomously with minimal supervision and deliver high-quality output on a consistent basis.

Nice to Have

• Prior Annotation Experience: Background in data entry, transcription, or annotation work is a strong plus.

•Annotation Platform Experience: Hands-on experience with specialized annotation tools and platforms.
 
•Machine Learning Development: Practical experience training models using frameworks such as scikit-learn, TensorFlow, or PyTorch.

•Data Visualization: Experience designing, building, and maintaining dashboards that make data accessible to non-technical audiences.

• Applied AI Awareness: Ongoing engagement with advancements in machine learning, deep learning, and generative AI, along with an eye for translating them into practical business applications.

Compensation and Benefits

• Exceptional Peers: Work alongside and learn directly from experienced practitioners in AI and data.

• Impactful Work: Contribute to research and projects that shape how the industry uses data and AI.

• State-of-the-Art Resources: Access to substantial computational infrastructure and modern tooling.

•Culture of Growth: A dynamic, intellectually stimulating environment that rewards curiosity and continuous learning.

•Flexible Working Arrangements: Set your own schedule around what works for you.