Gurgaon Data Science Architect 9 To 14 Years Gurgaon

Job description

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same. Job Description Owns the data science lifecycle end-to-end across multi-industry engagements - presales and opportunity shaping, greenfield ML/AI platform architecture, model development and productionization, and program governance through steady-state delivery, with a roadmap toward autonomous, self-optimizing AI/ML operations. Key Responsibilities • Lead presales engagements - RFP/RFI response, AI/ML solution architecture, effort estimation and commercial shaping - and present win themes to executive level stakeholders. • Own the end-to-end data science lifecycle - problem framing, exploratory data analysis, feature engineering, model development, validation and deployment. • Architect greenfield data and ML platforms (cloud-native data lakes/lakehouses, feature stores, MLOps pipelines), including vendor and tooling selection. • Define the roadmap toward autonomous, self-optimizing ML operations - automated retraining, drift detection and closed-loop model monitoring. • Lead large-scale brownfield data and analytics platform modernization and legacy-to-target migrations with minimal business disruption. • Partner with data engineering to ensure pipeline quality, governance and lineage, while personally owning model design, validation and business impact. • Own delivery governance for multi-year, multi-workstream AI/ML transformation programs - scope, schedule, risk, quality and financials. • Act as single technical point of accountability across design, build, deploy and operate phases, coordinating data engineering, MLOps and business teams. • Establish and track model and program KPIs (accuracy, drift, business impact), steering committee reporting and executive dashboards. • Mentor data science/delivery leads and build reusable accelerators, ML frameworks and playbooks across engagements. Technical Skills & Tools Category Tools / Technologies Languages & ML/DL Python, R, SQL, Scikit-learn, TensorFlow, PyTorch, XGBoost MLOps & Deployment MLflow, Kubeflow, Amazon SageMaker, Azure ML, Vertex AI Data Platforms Databricks, Snowflake, Apache Spark, Hadoop Cloud AWS, Azure, GCP Visualization Power BI, Tableau Generative AI LangChain, Azure OpenAI/OpenAI, RAG frameworks Frameworks & Program Delivery CRISP-DM, Agile/SAFe, TOGAF, MS Project, Jira, Confluence, PMP/Prince2 Primary Skills Data Science & ML # Experience in designing and deploying advanced analytics and AI solutions using traditional Machine Learning techniques including Classification, Regression, Clustering, Recommendation Systems, Anomaly Detection, Time Series Forecasting, and Reinforcement Learning. S