Senior Data Scientist- Senior Engineer, AVP

Description :The Deutsche India is seeking a talented and driven Senior Data Scientist to join our growing team. At the “Service Solutions and AI” Domain, our mission is to revolutionize our Private Bank process landscape by implementing holistic, front-to-back process automation. And being a Private Bank AI Centre of Excellence we are responsible for strategy building and execution of AI innovation, governance, and delivery across Private Bank, ensuring standardization, compliance, and accelerated adoption of AI solutions. We are dedicated to leveraging the power of data to drive innovation, optimize operations, and deliver exceptional value to our customers. We are committed to enhancing efficiency, agility, and innovation, with a keen focus on aligning every step of our process with the customer’s needs and expectations. Our dedication extends to driving innovative technologies, such as AI & workflow services, to foster continuous improvement. We aim to deliver “best in class” solutions across products, channels, brands, and regions, thereby transforming the way we serve our customers and setting new benchmarks in the industry. As a Senior Data Scientist , you will play a pivotal role in the full lifecycle of data science projects, from strategic problem definition and advanced data acquisition to robust model deployment and continuous performance monitoring. We are seeking a highly skilled and motivated individual with deep expertise in traditional data science methodologies, coupled with practical experience and a keen interest in Generative AI, Large Language Models (LLMs), Agentic AI, and Transformer architectures. You will work closely with cross-functional teams, including product managers, engineers, and business stakeholders, to identify transformative opportunities for data-driven and AI-powered solutions, translating complex business needs into innovative technical requirements. We are looking for someone passionately dedicated to uncovering hidden patterns in data, developing state-of-the-art solutions leveraging both conventional and Generative AI to solve various business problems, and effectively communicating complex findings to both technical and non-technical audiences. What we’ll offer you: As part of our flexible scheme, here are just some of the benefits that you’ll enjoy Best in class leave policy Gender neutral parental leaves 100% reimbursement under childcare assistance benefit (gender neutral) Sponsorship for Industry relevant certifications and education Employee Assistance Program for you and your family members Comprehensive Hospitalization Insurance for you and your dependents Accident and Term life Insurance Complementary Health screening for 35 yrs. and above Your key responsibilities Strategic Problem Framing & Generative AI Opportunity Identification: Business Problem Definition:  Define and frame complex business problems into clear, actionable data science questions, actively identifying and exploring opportunities where Generative AI, LLMs, and Agentic AI can deliver significant business value.Data Source Identification:  Strategically identify and evaluate the most relevant data sources to address defined business and AI problems. Advanced Data Management & Preparation: Data Acquisition & Wrangling:  Oversee and execute the collection, cleaning, and preprocessing of large and complex datasets from various internal and external sources, ensuring high data quality and readiness for both traditional ML and advanced Generative AI applications. Statistical Foundation & Data Understanding: Statistical Understanding:  Apply basic and advanced statistical understanding to thoroughly analyze and interpret data, ensuring robust insights and model validity.Exploratory Data Analysis (EDA):  Perform in-depth exploratory data analysis to understand data characteristics, identify critical trends, and formulate robust hypotheses for model development. Cutting-Edge Algorithm & Generative Model Development: Algorithm Research & Implementation:  Drive research and implementation of suitable Machine Learning (ML) algorithms and tools, staying at the forefront of the latest ML and Generative AI research. This includes selecting and customizing the most effective algorithms for specific business challenges, with a strong emphasis on Transformer-based architectures (e.g., BERT)  and Large Language Models (LLMs) .Model Development & Evaluation:  Architect, develop, implement, and rigorously evaluate advanced machine learning models (e.g., supervised, unsupervised, reinforcement learning) and Generative AI models (including LLMs and Agentic AI implementations)  to solve complex business problems, ensuring high performance, scalability, and interpretability. This includes specialized evaluation for generative outputs. Experimentation & Impact Measurement: Experiment Design & Hypothesis Testing:  Design and execute sophisticated experiments to test hypotheses and precisely measure the tangible impact of data-driven and Generative AI solutions on business outcomes. Effective Communication & MLOps Collaboration: Reporting & Strategic Presentation:  Communicate complex analytical findings and strategic recommendations, particularly for Generative AI applications, clearly and concisely to diverse audiences, including senior leadership and non-technical stakeholders, through compelling reports, interactive dashboards, and engaging presentations.MLOps Collaboration:  Collaborate effectively with engineering teams to deploy, maintain, and monitor data science and Generative AI models  in production environments, advocating for and contributing to MLOps best practices for operational excellence and lifecycle management of LLMs and Agentic systems. Continuous Innovation & Knowledge Sharing: Continuous Learning & Generative AI Innovation:  Continuously research and explore new data science techniques, Generative AI models, Agentic AI paradigms, advanced RAG techniques , tools, and technologies (e.g., new LLMs) to enhance organizational capabilities, foster innovation, and maintain a competitive edge.Knowledge Sharing & Community Engagement:  Actively contribute to the data science and AI community within the company by sharing advanced knowledge, best practices, and innovative approaches, mentoring junior team members in Generative AI. Your skills and experience Educational Foundation: Academic Background:  Graduate’s, Master’s or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, Engineering, or a related discipline. Extensive Professional Expertise: Data Science Leadership:  Proven experience (8+ years) in a data scientist role, demonstrating successful application of advanced data science techniques to solve complex, real-world problems. Technical Mastery & Generative AI Specialization: Programming Mastery:  Expert-level proficiency in programming languages commonly used in data science, particularly Python (with libraries like scikit-learn, pandas, NumPy) or R, SQL, etc.Data Analysis & EDA (MUST):  Proven expertise in data understanding, wrangling, analysis, and Exploratory Data Analysis (EDA).Statistics:  Hands-on experience and deep knowledge of statistical concepts such as Hypothesis testing, skewness, kurtosis, data distribution, central tendency, and their practical application.Core Data Science & ML Knowledge:  Solid understanding and execution knowledge of advanced statistical modeling, machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning), and experimental design.Generative AI Expertise (MUST):  Strong knowledge and hands-on experience in Generative AI, Large Language Models (LLMs), Transformer architectures (including BERT), LangChain, LangGraph, Agent Development Kits, and Agentic AI principles.RAG Implementation:  Practical experience in designing and implementing Retrieval Augmented Generation (RAG) systems.MLOps Familiarity:  Familiarity with MLOps practices and tools for deploying and managing ML models.Database Querying Skills:  Strong experience with SQL for efficient data extraction and manipulation from complex databases.Data Visualization Skills:  Advanced familiarity with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn) to effectively communicate complex insights.Cloud Platform Familiarity:  Familiarity with major cloud platforms (e.g., AWS, Azure, GCP) and their data science services, including practical deployment experience for AI/GenAI workloads.Big Data Experience (Nice to have):  Experience with big data technologies (e.g., Spark, Hadoop) is a significant advantage.Deep Learning Experience (Nice to have):  Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) is a significant plus. Leadership & Strategic Competencies: Communication & Presentation Skills:  Excellent communication and presentation skills, with the ability to clearly explain complex technical concepts (including Generative AI implications) to both technical and non-technical stakeholders.Analytical & Problem-Solving Prowess:  Superior analytical and critical problem-solving abilities, capable of navigating complex technical challenges and providing strategic, data-driven and AI-driven solutions.Work Ethic & Adaptability:  High work ethic and adaptability, thriving in a fast-paced environment and capable of working independently and collaboratively through evolving priorities and rapid advancements in the Generative AI landscape.Domain-Specific Expertise:  Preferred experience in the Banking & Finance Domain, with a deep understanding of industry-specific data and business challenges, and how Generative AI can be applied responsibly. How we’ll support you Training and development to help you excel in your career Coaching and support from experts in your team A culture of continuous learning to aid progression A range of flexible benefits that you can tailor to suit your needs