Current jobs related to Lead Data Scientist - Hadapsar - Deutsche Bank

Description The Deutsche India is seeking a talented and driven Lead 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 Lead Data Scientist , you will be at the forefront of our Generative AI initiatives, driving the full lifecycle of data science projects from strategic problem definition and advanced data acquisition to robust model deployment and continuous performance monitoring. We seek a visionary leader with deep expertise in traditional data science methodologies combined with a proven track record in Generative AI, Large Language Models (LLMs), Agentic AI, and Transformer architectures. You will work closely with cross-functional teams, including product managers, AI 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 and leveraging cutting-edge AI, especially the Gemini model, to deliver impactful solutions, effectively communicating 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:  Lead the definition and framing of complex business problems into clear, actionable data science questions, specifically identifying opportunities for Generative AI, LLMs, and Agentic AI. Strategically identify the most relevant data sources to address them. Advanced Data Management & Preparation for AI: Data Acquisition & Wrangling:  Oversee and execute the collection, cleaning, and preprocessing of large and complex datasets from various internal and external sources, ensuring data quality and readiness for both traditional ML and advanced Generative AI applications. Cutting-Edge Algorithm & Generative Model Development: Algorithm Research & Implementation:  Drive research and implementation of suitable Machine Learning (ML) algorithms, 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  and Large Language Models (LLMs) . Exploratory Data Analysis (EDA):  Lead in-depth exploratory data analysis to uncover data characteristics, identify critical trends, and formulate robust hypotheses for model development. Generative Model Development & Evaluation:  Architect, develop, implement, and rigorously evaluate advanced machine learning and Generative AI models (e.g., LLMs, Agentic AI, specifically leveraging models like Gemini) , 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 Leadership: 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 & Productionization Leadership:  Collaborate effectively with engineering teams to lead the deployment, maintenance, and monitoring of data science and Generative AI models  in production environments, championing MLOps best practices for operational excellence and lifecycle management of LLMs and Agentic systems. Continuous Innovation & Collaborative Leadership: Continuous Learning & Generative AI Innovation:  Continuously research and explore new data science techniques, Generative AI models, Agentic AI paradigms , tools, and technologies (e.g., advanced RAG techniques, 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. Agile Participation & Leadership:  Actively lead and contribute to all Agile ceremonies, including sprint planning, daily stand-ups, retrospectives, and refinements, driving efficiency, accountability, and continuous improvement within the squad. Cross-functional Collaboration:  Foster profound collaboration with cross-functional teams, including developers, product managers, AI engineers, and business stakeholders, to consistently achieve and exceed sprint and release objectives for both traditional and Generative AI projects. Product Advocacy:  Serve as a vocal advocate for data science and Generative AI products and solutions across the organization, continuously refining and pioneering product and process best practices within the squad and beyond. Your skills and experience Educational Foundation: Academic Background:  Bachelor’s, Master’s or PhD in a quantitative field such as Computer Science, Statistics, Mathematics, Engineering, or a related discipline. Extensive Professional Expertise: Leadership:  Proven experience (13+ years) in a data scientist role, demonstrating successful application of advanced data science and Generative AI techniques to solve complex, real-world problems, with a strong track record of leading technical initiatives. Technical Mastery & Generative AI Specialization: Programming & Data Science Proficiency:  Expert-level proficiency in programming languages commonly used in data science, particularly Python (with libraries like scikit-learn, pandas, NumPy) or R, SQL, etc. 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:  Deep and hands-on expertise in Generative AI, Large Language Models (LLMs), Transformer architectures, and Agentic AI principles. Specific Model Experience:  Demonstrated practical experience with leading LLMs, including the Gemini Model, and frameworks like LangChain, LangGraph, ADK etc. for building complex AI applications. RAG Implementation:  Proven experience in designing and implementing Retrieval Augmented Generation (RAG) systems with Vector Databases. Database Querying Skills:  Expert 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 Expertise:  Strong 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: Strategic Leadership & Influence:  Exceptional leadership, communication, and collaboration skills, with the ability to mentor junior engineers, influence cross-functional teams, and articulate complex technical concepts (including Generative AI implications) to both technical and non-technical stakeholders effectively. 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 leading teams through evolving priorities and rapid advancements in the Generative AI landscape. Proactive Innovation & Mentorship:  A proactive, results-oriented, and team-centric mindset with an unwavering commitment to continuous improvement, innovation, and technical excellence, actively fostering growth in others, particularly in emerging AI fields. 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.