Data Science and AI Unleashed:
Immerse yourself in the Center for Data Science and Artificial Intelligence practice, an enterprising analytics group within our organization. We’re on a dynamic trajectory, focused on devising inventive data-driven solutions across the enterprise. Unrestricted by boundaries, we’re backed by our substantial market presence in individual life insurance. Our canvas is vast, incorporating external data sources and advanced statistical techniques to create a new wave of predictive analytics and artificial intelligence solutions.
We’re pioneering a new era with multivariate model-based continuous risk differentiation, rewriting the industry’s norms. Devising models for targeted advertising allocation, geographic analytics, and more, we’re adding remarkable value. Our products propel real-time business processes – underwriting, pricing, prospecting, and beyond.
Collaborate with Data Scientists and MLOps Engineers to infuse software engineering best practices into the Model Development Lifecycle (MDLC). Set the standard for ML engineering, and oversee the work of others. Become a pivotal part of our MLOps team, guided by Boris Simanovich.
- Develop production-ready Machine Learning solutions.
- Ensure ML code adheres to software engineering best practices.
- Shape ML engineering standards and practices.
- Implement models with scalability, monitoring, and debugging in mind.
- Mentor engineers and data scientists in applying software engineering to ML challenges.
- Setup ML experiments for server-based hyperparameter tuning.
- Work with MLOps Engineers to automate model deployment pipelines.
- Collaborate on solution architectures, integrating ML models into real-world systems.
- Stay current with AI trends and seek ways to enhance the stack.
- Effectively communicate complex ideas to diverse audiences.
- 7+ years as a Lead Software Engineer, with at least 3 years of hands-on AI/ML implementation experience.
- Expertise in Python, including prominent ML libraries (e.g., sklearn, TensorFlow, PyTorch, Keras).
- Familiarity with containerization (Docker) and Linux.
- Optimizing/tuning models and understanding of GPU acceleration techniques.
- Passionate about technology transformations and continuous innovation.
- Proficiency in NLP techniques and tools (SpaCy, NLTK, Hugging Face).
- Strong understanding of data structures, algorithms, and software engineering principles.
- Experience with SQL and big data platforms (Postgres, Redshift, Snowflake).
- Familiarity with cloud environments (AWS) and cloud-native tools.
- Agile/Scrum methodology expertise.
- Graduate-level degree in computer science, engineering, or equivalent work experience.
- Previous work with Generative AI.
- Understanding of Vector Databases and their implementation.
- Kubernetes container orchestration familiarity.
- Experience in the insurance industry.