Data Scientist

We are seeking an experienced Senior/Principal Data Scientist & Machine Learning Engineer to lead high-impact AI/ML initiatives across diverse business domains. This role combines deep technical expertise, strategic thinking, and hands-on engineering to design, build, and deploy scalable machine learning, deep learning, and agentic AI solutions. The ideal candidate excels at translating ambiguous business problems into analytical frameworks, leading end-to-end ML pipelines, and influencing cross-functional teams and executives through data-driven insights. Key ResponsibilitiesAI/ML Strategy & Leadership • Define technical strategy for AI/ML initiatives, identify high-value use cases, and drive innovation across business units. • Lead end-to-end project execution—problem definition, data exploration, model development, validation, deployment, monitoring, and continuous improvement. • Mentor data scientists and ML engineers; promote best practices in modeling, experimentation, MLOps, and reproducible research. • Influence executive decision-making with clear communication of analytical insights and model outcomes. Modeling & Advanced Analytics • Design and develop predictive, optimization, forecasting, NLP, computer vision, and deep learning models using structured/unstructured data. • Perform experimental design, A/B tests, causal inference, statistical modeling, and hypothesis testing. • Build large-scale ML pipelines, agentic AI systems, LLM fine-tuning, and advanced deep learning architectures. • Apply scalable big data processing (e.g., PySpark, distributed computing) for terabyte-scale datasets. Engineering, MLOps & Deployment • Build and optimize end-to-end ML systems including data ingestion, preprocessing, hyperparameter tuning, and continuous deployment. • Deploy models in production via cloud ML platforms (AWS SageMaker, Azure ML, OCI, GCP) and containerized environments (Docker, Kubernetes). • Implement CI/CD for ML, model versioning, monitoring, retraining workflows, and performance optimization. • Develop prototypes/MVPs and production-grade model services using Python, .NET, C#, or Java. Cross-Functional Collaboration • Partner with business, product, sales, finance, engineering, and executives to align AI initiatives with strategic goals. • Translate complex technical findings into actionable recommendations for non-technical audiences. • Support analytics initiatives across domains such as supply chain, sales optimization, customer retention, automotive, finance, or logistics. Data Management & Governance • Collaborate with data engineering to ensure scalable, reliable pipelines, instrumentation, and data quality. • Work with large, complex, multi-source datasets; perform ETL/ELT and enforce data hygiene standards. Required Qualifications • Master’s or Ph.D. in Computer Science, Data Science, Statistics, Engineering, Economics, or related quantitative field (or equivalent experience). • 7–12+ years of experience in data science, machine learning, or AI engineering, with demonstrated end-to-end project delivery. • Advanced proficiency in Python, SQL, and ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost). • Experience with LLMs, deep learning, agentic AI, and transformer architectures. • Expertise in big data technologies (PySpark, distributed computing) and data pipeline development. • Strong grounding in statistical modeling, experimental design, causal inference, and optimization. • Hands-on experience with Docker, CI/CD, MLOps, cloud platforms (AWS, Azure, GCP, OCI). • Excellent communication skills and ability to influence technical and non-technical stakeholders. • Proven ability to architect scalable ML solutions and optimize models for performance, reliability, and cost-efficiency. Preferred Qualifications • Experience in domains such as supply chain, automotive, finance, sales analytics, or logistics. • Familiarity with workflow orchestration tools (Airflow, Kubeflow). • Experience handling real-time data, streaming analytics, and large-scale data architectures. • Publications, patents, or contributions to high-impact research.