IN_Manager_Data Science_D&A_Advisory_PAN India

Job Description & Summary: PwC India is seeking an experienced and results-driven Data Science Manager to lead the design, development, and deployment of advanced analytics, machine learning, and AI-driven solutions that address complex business challenges. This role requires a blend of strong technical expertise, business acumen, and leadership capabilities to manage cross-functional teams, drive data-driven decision-making, and deliver scalable data science solutions across diverse industry sectors. The candidate will collaborate with business stakeholders, data engineers, architects, and technology teams to build innovative analytics products that generate measurable business value. Job Position Title: IN_Manager_Data Science_D&A_Advisory_PAN India Responsibilities: Lead and manage end-to-end data science engagements, from problem definition and data exploration to model development, deployment, and monitoring. Design and develop advanced machine learning, predictive analytics, NLP, generative AI, and statistical models to solve complex business problems. Collaborate with business stakeholders and leadership teams to identify opportunities where analytics and AI can drive business impact and operational efficiency. Build, mentor, and manage high-performing teams of data scientists, ensuring technical excellence and continuous learning. Develop scalable AI/ML solutions using cloud platforms such as Azure, AWS, and GCP, leveraging cloud-native AI services and frameworks. Work closely with data engineering teams to establish robust data pipelines, feature stores, model deployment processes, and MLOps frameworks. Drive model governance, explainability, fairness, and compliance while ensuring adherence to responsible AI practices. Present analytical findings, business insights, and strategic recommendations to senior leadership and client stakeholders. Lead AI and data science solution architecture discussions, selecting appropriate algorithms, tools, and deployment approaches. Monitor model performance, conduct continuous improvements, and implement retraining strategies to maximize business value. Support business development activities by contributing to proposals, solution design, client presentations, and thought leadership initiatives. Stay current with emerging trends in AI, machine learning, generative AI, and advanced analytics, bringing innovative solutions to clients. Mandatory Skill Sets: Strong hands-on experience in Machine Learning, Statistical Modeling, Predictive Analytics, and AI solution development. Expertise in programming languages such as Python and SQL, along with common data science libraries including Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and XGBoost. Experience deploying and managing machine learning solutions on cloud platforms including Microsoft Azure, AWS, or Google Cloud Platform. Strong understanding of supervised and unsupervised learning, feature engineering, model evaluation, optimization, and explainability techniques. Hands-on experience with Generative AI, Large Language Models (LLMs), prompt engineering, Retrieval Augmented Generation (RAG), and AI orchestration frameworks. Proficiency in data visualization and storytelling using tools such as Power BI, Tableau, Matplotlib, or Plotly. Experience implementing MLOps practices including model deployment, versioning, monitoring, and automation. Strong stakeholder management, team leadership, and project management capabilities. Ability to translate business requirements into scalable analytics and AI solutions. Strong understanding of data governance, model risk management, responsible AI, and compliance frameworks. Preferred Skill Sets: Experience with Azure AI Services, Azure Machine Learning, AWS SageMaker, Google Vertex AI, or equivalent AI platforms. Knowledge of NLP techniques including sentiment analysis, text classification, summarization, and conversational AI applications. Experience working with big data technologies such as Spark, Databricks, Hadoop, or distributed computing frameworks. Exposure to containerization and orchestration technologies including Docker and Kubernetes. Experience with modern MLOps and DevOps tools such as MLflow, Kubeflow, GitHub Actions, Azure DevOps, or Jenkins. Knowledge of vector databases, knowledge graphs, and enterprise AI architectures. Prior consulting experience in industry domains such as Financial Services, Healthcare, Retail, Manufacturing, or Technology. Professional certifications in cloud platforms, AI, Machine Learning, or Data Science would be an added advantage. Years of Experience Required: 8-12 Years Education Qualification: BE / <B.Tech> / MCA / <M.Tech> / MBA (Analytics, Data Science, AI)