Lead Data Scientist (DAT00844)

Location

Bangalore | India

Job description

Job Description

Role and Responsibilities:

As a Lead Data Scientist at Acko Insurance, you will be a Subject Matter Expert, at the forefront of crafting and deploying advanced machine-learning models to solve complex problems in the insurance domain. Your role will focus on customer-centric problem-solving, diving deep into data and use cases, and deploying scalable AI solutions. An ideal candidate will have a stellar background in machine learning, and experience with production-level AI deployments, particularly in B2C products. You are comfortable putting together a quick prototype as well as reading research papers to inspire your solutions.

Key Responsibilities:

• Spearheaded the development of sophisticated machine learning models, with a keen eye for practical application and great intuition for its performance in production.
• Deploy production-ready AI systems, ensuring robustness, scalability, and alignment with business goals.
• Utilize a broad array of ML modeling techniques, modern NLP techniques, large language models (LLMs) in day-to-day work.
• Champion the integration of Cloud AI models and familiarity with LLMs of various types, to create both standalone solutions as well as AI-as-a-feature
• Develop and maintain detailed documentation and version control of models, algorithms, and deployed systems.
• Collaborate with data engineers to streamline data pipelines and ensure efficient data flow for model training and inference.
• Work alongside Product Managers to set the right performance expectations from AI models, do RCA and provide resolution to errors, and help define testing methodologies.
• Participate in review of system performance and perform resolution of business and product queries independently

Technical Requirements:

• Minimum 6 years of hands-on experience in data science, with a proven track record in deploying AI models in production environments.
• Advanced proficiency in Python and its data science libraries (e.g., pandas, sci-kit-learn, TensorFlow, PyTorch).
• Strong foundation in a variety of machine learning techniques, including but not limited to ensemble methods, decision trees, and regression analysis.
• Experience with NLP and the use of LLMs to drive understanding and insights from textual data.
• Familiarity with cloud-based AI services and tuning open-source models, in building solutions is useful but not mandatory.
• Direct experience in the insurance sector with an understanding of industry-specific data challenges.
• A Bachelor's or Master's degree in Engineering with a flair for Computer Science concepts, Mathematics, Statistics, or a related technical field is preferred.

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