IN_Manager_Data Scientist Gen AI_Data & Analytics_Advisory_Kolkata

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.

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.

Responsibilities:

Position responsibilities and expectations · Designing and building analytical /DL/ ML algorithms using Python, R and other statistical tools. · Strong data representation and lucid presentation (of analysis/modelling output) using Python, R Markdown, Power Point, Excel etc. · Ability to learn new scripting language or analytics platform. Technical Skills required (must have) · HandsOn Exposure to Generative AI (Design, development of GenAI application in production) · Strong understanding of RAG, Vector Database, Lang Chain and multimodal AI applications. · Strong understanding of deploying and optimizing AI application in production. · Strong knowledge of statistical and data mining techniques like Linear & Logistic Regression analysis, Decision trees, Bagging, Boosting, Time Series and Non-parametric analysis. · Strong knowledge of DL & Neural Network Architectures (CNN, RNN, LSTM, Transformers etc.) · Strong knowledge of SQL and R/Python and experience with distribute data/computing tools/IDEs. · Experience in advanced Text Analytics (NLP, NLU, NLG). · Strong hands-on experience of end-to-end statistical model development and implementation · Understanding of LLMOps, ML Ops for scalable ML development. · Basic understanding of DevOps and deployment of models into production (PyTorch, TensorFlow etc.). · Expert level proficiency algorithm building languages like SQL, R and Python and data visualization tools like Shiny, Qlik, Power BI etc. · Exposure to Cloud Platform (Azure or AWS or GCP) technologies and services like Azure AI/ Sage maker/Vertex AI, Auto ML, Azure Index, Azure Functions, OCR, OpenAI, storage, scaling etc. Technical Skills required (Any one or more) · Experience in video/ image analytics (Computer Vision) · Experience in IoT/ machine logs data analysis · Exposure to data analytics platforms like Domino Data Lab, c3.ai, H2O, Alteryx or KNIME · Expertise in Cloud analytics platforms (Azure, AWS or Google) · Experience in Process Mining with expertise in Celonis or other tools · Proven capability in using Generative AI services like OpenAI, Google (Gemini) · Understanding of Agentic AI Framework (Lang Graph, Auto gen etc.)

· Understanding of fine-tuning for pre-trained models like GPT, LLaMA, Claude etc. using LoRA, QLoRA and PEFT technique. · Proven capability in building customized models from open-source distributions like Llama, Stable Diffusion