Junior Data Scientist

The Junior Data Scientist supports the design, development, and operationalization of machine learning, advanced analytics, and Generative AI solutions for ZainTECH’s enterprise customers. The role also supports customer-facing delivery activities, including workshops, demonstrations, and proof-of-concept engagements, while building the technical and consulting capabilities required to progress within ZainTECH’s Data & AI Practice.

Responsibilities:

Machine Learning & Data Science

• Develop, train, test, and evaluate machine learning models for classification, regression, forecasting, NLP, and other enterprise use cases under the guidance of senior team members.
• Perform data preparation, exploratory data analysis, feature engineering, and model evaluation to support the development of effective data science solutions.
• Build and maintain reproducible pipelines for data preparation, feature engineering, and model training.
• Apply statistical techniques and appropriate model evaluation methodologies to validate solution performance and business relevance.

Generative AI & Emerging Technologies

• Contribute to the development of Generative AI solutions using Large Language Models (LLMs) and foundation models.
• Support prompt engineering, embeddings, vector databases, Retrieval-Augmented Generation (RAG) pipelines, and LLM evaluation.
• Integrate foundation models, including Azure OpenAI and open-source LLMs, into enterprise applications and workflows.
• Gain hands-on experience with modern GenAI frameworks such as LangChain, LangGraph, and related technologies.
• Support the evaluation and continuous improvement of GenAI solutions based on performance, accuracy, and customer requirements.

ModelOps & Solution Operationalization

• Support the full machine learning model lifecycle, including experiment tracking, model versioning, packaging, deployment, monitoring, and retraining.
• Apply ModelOps/MLOps practices and tools such as MLflow, model registries, CI/CD pipelines, and containerized model serving.
• Monitor deployed models for drift, performance degradation, and data quality issues.
• Assist in developing monitoring, alerting, and remediation processes to maintain model performance in production environments.
• Collaborate with DevOps and engineering teams to support the reliable deployment and operation of AI solutions.

Solution Development & Integration

• Work closely with Data Engineers, ML Engineers, DevOps Engineers, and Application Developers to integrate models into end-to-end enterprise solutions.
• Support the development of APIs and lightweight applications to expose machine learning models and GenAI capabilities where required.
• Work with structured and unstructured data across different data sources and platforms.
• Contribute to solutions deployed across cloud and enterprise AI platforms, with a particular focus on Microsoft Azure.

Customer Delivery & Documentation

• Participate in customer workshops, demonstrations, and proof-of-concept engagements as part of the Data & AI delivery team.
• Support senior team members in translating customer requirements into practical data science and AI solutions.
• Communicate technical findings and model outputs clearly to technical and non-technical stakeholders.
• Document solutions, experiments, methodologies, and operational runbooks to production standards.
• Contribute to knowledge-sharing and continuous improvement initiatives within the Data & AI Practice.

Requirements

• Up to 3 years of hands-on experience in data science, machine learning, or a related field. Relevant internships and significant academic or personal projects will be considered.
• Strong Python programming skills and familiarity with common data science and machine learning libraries, including: pandas, scikit-learn, PyTorch and/or TensorFlow.
• Working knowledge of ModelOps/MLOps concepts and tools, including experiment tracking, model registries, CI/CD for machine learning, containerized model serving, and model monitoring.
• Practical exposure to Generative AI concepts and technologies, including: LLM APIs, Prompt engineering, Embeddings and vector databases, RAG architectures, LangChain, LangGraph, or similar frameworks.
• Solid understanding of statistics, experimental design, and model evaluation methodologies.
• Proficiency in SQL with the ability to work with structured and unstructured data.
• Good written and verbal communication skills in English, with the ability to explain technical concepts and results to non-technical stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related discipline.