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 ScienceDevelop, train, test, and evaluate machine learning models for classification, regression, forecasting, NLP, and other enterprise use cases under the guidance of senior team membersPerform data preparation, exploratory data analysis, feature engineering, and model evaluation to support the development of effective data science solutionsBuild and maintain reproducible pipelines for data preparation, feature engineering, and model trainingApply statistical techniques and appropriate model evaluation methodologies to validate solution performance and business relevanceGenerative AI & Emerging TechnologiesContribute to the development of Generative AI solutions using Large Language Models (LLMs) and foundation modelsSupport prompt engineering, embeddings, vector databases, Retrieval-Augmented Generation (RAG) pipelines, and LLM evaluationIntegrate foundation models, including Azure OpenAI and open-source LLMs, into enterprise applications and workflowsGain hands-on experience with modern GenAI frameworks such as LangChain, LangGraph, and related technologiesSupport the evaluation and continuous improvement of GenAI solutions based on performance, accuracy, and customer requirementsModelOps & Solution OperationalizationSupport the full machine learning model lifecycle, including experiment tracking, model versioning, packaging, deployment, monitoring, and retrainingApply ModelOps/MLOps practices and tools such as MLflow, model registries, CI/CD pipelines, and containerized model servingMonitor deployed models for drift, performance degradation, and data quality issuesAssist in developing monitoring, alerting, and remediation processes to maintain model performance in production environmentsCollaborate with DevOps and engineering teams to support the reliable deployment and operation of AI solutionsSolution Development & IntegrationWork closely with Data Engineers, ML Engineers, DevOps Engineers, and Application Developers to integrate models into end-to-end enterprise solutionsSupport the development of APIs and lightweight applications to expose machine learning models and GenAI capabilities where requiredWork with structured and unstructured data across different data sources and platformsContribute to solutions deployed across cloud and enterprise AI platforms, with a particular focus on Microsoft AzureCustomer Delivery & DocumentationParticipate in customer workshops, demonstrations, and proof-of-concept engagements as part of the Data & AI delivery teamSupport senior team members in translating customer requirements into practical data science and AI solutionsCommunicate technical findings and model outputs clearly to technical and non-technical stakeholdersDocument solutions, experiments, methodologies, and operational runbooks to production standardsContribute to knowledge-sharing and continuous improvement initiatives within the Data & AI PracticeOur 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.RequirementsUp 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 consideredStrong Python programming skills and familiarity with common data science and machine learning libraries, including: pandas, scikit-learn, PyTorch and/or TensorFlowWorking knowledge of ModelOps/MLOps concepts and tools, including experiment tracking, model registries, CI/CD for machine learning, containerized model serving, and model monitoringPractical exposure to Generative AI concepts and technologies, including: LLM APIs, Prompt engineering, Embeddings and vector databases, RAG architectures, LangChain, LangGraph, or similar frameworksSolid understanding of statistics, experimental design, and model evaluation methodologiesProficiency in SQL with the ability to work with structured and unstructured dataGood written and verbal communication skills in English, with the ability to explain technical concepts and results to non-technical stakeholdersBachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related discipline