Data Scientist/Engineer Intern

We're looking for a curious, detail-oriented graduate to join our team in a hands-on role spanning the full data lifecycle, from annotation and preparation through pipeline development, analysis, and machine learning. This is an entry-level opportunity designed for fresh graduates and diploma holders who want broad exposure across data engineering, analytics, and AI, working alongside experienced practitioners on projects that shape how our organization uses data. If you're eager to build a strong technical foundation, learn quickly, and take real ownership of your work, we'd love to hear from you.

• 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.

• 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.

• 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.

• 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.