Data Scientist - AI, ML & Agentic Automation

Overall Experience: 7+ years\n\nWork mode: Hybrid/ Remote\n\nAbout the role:\nCybage Software is looking for a highly analytical and solution-oriented Data Scientist to design, develop, and deploy scalable AI/ML and automation solutions for global clients and leading brand portfolios.\nThis role sits at the intersection of data science, advanced analytics, automation, and emerging agentic AI systems , helping transform complex business challenges into impactful, production-ready solutions.\nYou will work closely with architects, engineers, analytics teams, and business stakeholders to build intelligent systems that drive measurable business outcomes.\n\nKey Responsibilities:\nDesign and develop scalable Data Science, Machine Learning, and automation solutions.\nBuild predictive models, analytical frameworks, and intelligent workflows using modern DS/ML techniques.\nExplore and apply emerging technologies in AI, automation, agentic AI, and data enrichment.\nPerform data preparation, feature engineering, hypothesis testing, experimentation, and model evaluation.\nTranslate business problems into robust analytical solutions and actionable insights.\nWork with large-scale, multi-source datasets while ensuring data quality, governance, and reliability.\nCollaborate with engineering teams to productionize models and automation pipelines.\nMonitor model performance, troubleshoot issues, and continuously optimize solutions.\nCommunicate technical findings and recommendations effectively to both technical and non-technical stakeholders.\n\nRequired Skills & Experience:\n4+ years of experience in Data Science, Applied Analytics, or ML Engineering.\nStrong hands-on experience with: Python (Pandas, NumPy, Scikit-learn, SciPy, Statsmodels), SQL and large-scale data handling, Statistical modelling and Machine Learning techniques\nExperience with: Data preparation and feature engineering, Predictive modelling and experimentation, Automation workflows and AI-driven analytics, Git/version control and collaborative development workflows\nUnderstanding of modern AI/GenAI concepts such as: LLMs, RAG pipelines, Agentic AI workflows, Intelligent automation\nExperience with audience analytics, customer insights, or marketing analytics is preferred.\nFamiliarity with data visualization and reporting platforms beyond Power BI/Tableau.\nStrong problem-solving, communication, and stakeholder management skills.\nBachelor’s or Master’s degree in Statistics, Mathematics, Economics, Computer Science, Engineering, or related quantitative fields.\n\nGood to Have:\nExposure to AWS, Azure, or GCP.\nExperience with MLOps, CI/CD, or production model deployment.\nKnowledge of data governance, data quality frameworks, and platform integration practices.