• Your role will be pivotal in connecting complex data ecosystems and AI-ML models to solve real-world challenges. Collaborate with a dynamic team of Data Scientists and AI-ML experts in crafting and implementing AI-driven solutions that not only enhance our operational efficiencies but also set new benchmarks in industry innovation.
• Dive into large, intricate datasets, and leverage your skills to make a significant, measurable impact in a field where your work will be seen and felt
• Collaborate with platform and data engineering teams to ensure seamless integration and deployment of models into production systems

How You’ll Do It

• Feature Engineering: Develop and maintain efficient data pipelines that source structured and unstructured data from various sources, ensuring feature integrity, availability, and optimization. Utilize NLP and generative AI capabilities to structure unstructured data to be used for ML model development. Collaborate with platform and data engineering teams to ensure seamless integration and deployment of models into production systems. 
• Feature Discovery: Build tools and accelerators for feature construction and discovery, leveraging advanced ML and generative AI capabilities
• Feature Foundry: In collaboration with MLOps and Data Engineering teams, design, prototype and implement feature repository with time-traveling capability. 
• Accelerators: leverage open-source and proprietary techniques for building accelerators for AI-ML solution development and deployment
• ML System Design: Work with Data Scientists and ML Engineers to gather requirements, architect, design, and help implement data-driven systems and platforms ensuring scalability, efficiency, and robustness. 
• Leadership: Drive data-driven initiatives from conception to execution. Collaborate with cross-functional teams to ensure alignment, efficacy, and timeliness. 
• Communication: Translate complex findings into understandable insights and present them to peers, leadership, and business stakeholders. 
• Machine Learning: Develop and deploy unsupervised and supervised machine learning, and generative AI techniques to construct and validate predictive features from text, image, and structured data
• Research & Development: Stay abreast of the latest developments in machine learning, and generative AI, incorporating new techniques and methodologies into our processes to keep us ahead in the insurance industry.

The Right Stuff

A Master’s Degree in Data Science, Computer Science, Statistics, Mathematics, or a related field and at least 7 years of relevant work experience or a Bachelor’s Degree in these fields with at least 9 years of relevant work experience.

• 7 years experience with and solid proficiency in a programming language such as Python and SQL
• 4 years experience with data orchestration workflow tools such as DBT, Airflow etc
• 3 years experience in building feature repository for training AI-ML models
• 2 years experience in data scraping 
• 2 years experience in processing text and image data using parallel processing technique
• 4 years experience working with public cloud ML capabilities, preferably AWS, Databricks, Docker, Kubernetes 
• 2 years experience in design and build of model-driven dashboarding and UI such as streamlit, Dash
• Exposure to machine learning techniques and predictive modeling

Key Competencies

• Creativity: Able to think outside the box to find innovative solutions to complex problems. 
• Intellectual Curiosity: Passionate about learning and staying updated with the latest developments in the field.
• Attention to Detail: Ensures precision and accuracy in all tasks and projects. 
• Adaptability: Thrives in a fast-paced environment, adapting to changing business needs. 
• Interpersonal Sensitivity: Works effectively in team settings, valuing and respecting the views and roles of others. 
• Decision-Making: Makes sound decisions based on data, analysis, and experience. 
• Strategic Thinking: Can envision long-term strategies and align day-to-day activities towards achieving them.

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