The Senior Manager, Data Science (Generative AI) role demands a robust technical foundation, primarily in Generative AI. This includes deep expertise in Deep Learning and Machine Learning algorithms, practical experience with Generative AI technologies and frameworks, and specialized skills in Prompt Engineering, Retrieval Augmented Generation (RAG), Langgraph, Langchain frameworks and Large Language Model (LLM) fine-tuning. Candidates must be adept at Generative AI solution design and implementation. Complementing this, strong data science technical skills are essential, encompassing proficiency in programming languages like Python, experience with data manipulation tools such as PySpark, SQL, and Hive, and the ability to handle data requirements, collection, processing, cleaning, exploratory data analysis, and effective data visualization. A solid understanding of analytical and statistical foundations, including hypothesis testing, segmentation, predictive modeling, and performance optimization, is also crucial.
Beyond technical expertise, the role requires significant leadership and management capabilities, including the ability to lead, mentor, and develop a team of data scientists. Strategic acumen is paramount, demanding a candidate who can demonstrate strategic thinking and vision, identify emerging trends, challenge the status quo, and drive innovation to set ambitious business goals. Excellent communication and stakeholder management skills are also vital for effectively translating complex technical concepts and performance metrics for senior business audiences and ensuring strong collaboration with both team members and business partners. Apart from these, they should also have the following skillset:
Responsibilities:
- Lead the architecture, design, and development of mission-critical, production-grade Generative AI applications and Agentic solutions, ensuring they are scalable, reliable, and secure.
- Define and own the technical roadmap for AI platforms, making critical decisions on architecture, technology stacks, and long-term evolution.
- Drive the strategy for designing and redesigning business processes with Agentic solutions, focusing on advanced agent architecture, memory management, hallucination control, and reinforcement learning-based feedback loops.
- (For Manager Track - VP): Lead and mentor a team of engineers and data scientists, fostering a culture of innovation and engineering excellence. For VPs, this includes formal people management, career development, and team building.
- Champion and enforce best practices for compliance, security, MLOps, and Model Governance across all AI projects.
- Engage with senior stakeholders to translate business needs into technical strategy and communicate technical vision and project status.
- Stay at the forefront of AI research and industry trends, identifying new opportunities and technologies to drive business value.
Qualifications:
- 10-15 years of overall experience with a minimum of 3 years leading the development of production-ready Gen AI RAG or Agent-based solutions.
- Extensive experience as a full-stack development manager or architect, with a proven track record of delivering highly scalable and reliable systems.
- Deep expertise in architecting and designing complex Agentic solutions, including a rich understanding of different agentic architectures and their trade-offs.
- Mastery of agent training, performance measurement, and feedback processes, with the ability to create and articulate new architectural patterns.
- Strong people management skills with experience in grooming and developing engineers (mandatory for manager track).
- Expertise in program/project management, Agile processes, and managing development timelines for large-scale projects.
- Deep knowledge of financial services compliance, security, and performance management.
- Recognized as a subject matter expert in Python, modern AI/ML frameworks, MLOps, and cloud-native technologies.
Education:
- Bachelor's/University degree; Master's degree highly preferred in Computer Science, AI, Engineering, or a related quantitative field.
Technical Proficiency:
- Hands-on experience in applied machine learning, Generative AI solutions (e.g., Prompt Engineering, Retrieval Augmented Generation (RAG), Google ADK, Langgraph, Langchain frameworks and Big Data technologies.
- Proficiency in scripting languages (e.g., Python) and key libraries.
- Strong expertise in deep learning and machine learning algorithms, especially those applicable to Generative AI (e.g., large language models, diffusion models), data analysis, and pattern recognition.
Candidates with the following additional qualifications will be highly regarded.
- Platform Experience: Experience with Langgraph, Langchain frameworks, Cloud environments, Spark, and Hive.
- Communication: Exceptional written and oral communication skills, with a proven ability to simplify complex technical methods for non-expert audiences.
- Development Tools: Experience with shell scripting, bash, and Git/GitHub for version control and collaboration.
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Job Family Group:
Decision Management
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Job Family:
Specialized Analytics (Data Science/Computational Statistics)
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Time Type:
Full time
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Most Relevant Skills
Please see the requirements listed above.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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