Data Analyst / Scientist

Overview

• Develop, train, and optimize machine learning models for classification, regression, forecasting, clustering, NLP, and deep learning use cases.
• Build AI-driven solutions such as recommendation engines, anomaly detection systems, predictive scoring models, and generative AI applications.
• Perform end‑to‑end data science workflows including data acquisition, cleaning, feature engineering, model selection, validation, and deployment.
• Collaborate with data engineers to design scalable data pipelines and ensure high‑quality data availability for modelling.
• Implement MLOps practices such as model versioning, monitoring, retraining, and performance tracking.
• Conduct statistical analysis, experiment design, and A/B testing to validate model effectiveness and business impact.
• Work with cloud platforms (Azure, AWS, or GCP) to train, deploy, and operationalize ML models.
• Communicate insights, model results, and recommendations to technical and non‑technical stakeholders through dashboards, reports, and presentations.
• Integrate models into business applications using APIs, cloud services, or automation frameworks.
• Stay updated on emerging AI/ML technologies, LLMs, and best practices to continuously improve modeling approaches.