IN_Senior Associate_Python_Data and Analytics_ Advisory_Bangalore

Job description

**Line of Service** Advisory **Industry/Sector** Not Applicable **Specialism** Operations **Management Level** Senior Associate **Job Description & Summary** At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals. In data analysis at PwC, you will focus on utilising advanced analytical techniques to extract insights from large datasets and drive data-driven decision-making. You will leverage skills in data manipulation, visualisation, and statistical modelling to support clients in solving complex business problems. \\*Why PWC At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us . At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. " **About the Role** We're looking for a Senior AI/ML Engineer who can design, build, and deploy scalable ML, GenAI, and Agentic AI systems across cloud environments (GCP preferred) with strong focus on productionization , automation, and business impact. You'll work across demand forecasting, RAG-based intelligent applications, autonomous multi-agent systems, and enterprise AI integration. **Responsibilities** - Build end-to-end ML/AI pipelines (data model deployment monitoring) - Develop and deploy ML, Deep Learning, NLP, and GenAI models in production - Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering - Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory - Build and optimize time series forecasting models (demand forecasting, inventory planning) - Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance - Optimize models for performance, cost, and latency - Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications - Design scalable LLM inference architectures for efficient deployment - Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams - Debug, optimize , and enhance ML models for quality and performance improvements - Mentor team members and present technical findings to diverse audiences - Stay current with AI/GenAI trends and evaluate emerging tools and frameworks **Mandatory Skills** **1. Programming & Core** - Python — strong, production-grade coding - SQL — proficient - Data Structures & Algorithms - Git **2. Machine Learning & Deep Learning** - Regression, Classification, Clustering, Dimensionality Reduction - Ensemble Models (Random Forest, XGBoost , LightGBM ) - CNN, RNN, LSTM, Transformers - Frameworks: Scikit-learn, XGBoost , LightGBM , TensorFlow, Keras , PyTorch **3. Statistics & Mathematics** - Probability (Bayesian, Frequentist), Hypothesis Testing, A/B Testing - Regression (Linear, Logistic, GLM), Time Series Analysis - Optimization (convex/non-convex) - Libraries: NumPy, SciPy, Statsmodels **4. ML Pipelines &** **MLOps** - Building end- to- end ML pipelines in production (training, serving, monitoring) - MLOps tools: MLflow , Kubeflow, Vertex AI Pipelines - Model monitoring, drift detection, and governance **5. Cloud — GCP (Primary)** - Vertex AI (model training, pipelines, endpoints) - BigQuery , Cloud Storage, Dataproc ( PySpark ) - Cloud Composer (Airflow), Cloud Run **6. Generative AI & LLMs** - LLMs, advanced prompt engineering - RAG pipelines — retrieval, chunking, embeddings, vector search - VectorDBs : FAISS, Pinecone, Weaviate , ChromaDB , pgvector - Frameworks: LangChain , LlamaIndex , Hugging Face Transformers **7. Agentic AI** - Autonomous agents, multi-agent systems - Tool calling, planning, memory, workflow orchestration - Frameworks: LangGraph , CrewAI , AutoGen - Protocols: MCP (Model Context Protocol), A2A (Agent-to-Agent) **8. Time Series / Demand Forecasting** - Experience building forecasting models for business prediction - Time series techniques and retail/supply chain forecasting **9. NLP** - Text preprocessing, embeddings, NER, classification, sentiment analysis - Semantic search - Frameworks: Hugging Face Transformers, spaCy , NLTK **10. Soft Skills** - Strong communication — can present technical concepts to non-technical audiences - Mentoring ability — can guide and uplift junior team members - Analytical thinking with ability to translate business problems into AI solutions - Comfortable working in Agile, cross-functional teams **Good to Have** - LLM fine-tuning ( LoRA , PEFT, or full fine-tune on Vertex AI) - LLM serving & inference optimization ( vLLM , GPU memory optimization, model quantization) - Spark / PySpark for large-scale data processing - Computer Vision (image classification, object detection, OCR/Document AI, YOLO, Detectron2) - Recommendation Systems (collabor