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.

Our Culture & Code of Conduct:

At ZainTECH, we take pride in a culture built on collaboration, innovation, and uncompromising integrity. We are looking for individuals who share these values and are committed to customer-centricity and ethical excellence. All employees are expected to uphold our Code of Conduct, which serves as a guiding framework for responsible behavior across everything we do — from how we work with each other to how we engage with clients and partners globally.

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.