5+ years of experience working as a GenAI Data Science.
Experience with Python from a functional programming paradigm, able to manage dependencies and virtual environments, along with version control in git
Experience with sequential algorithms (e.g., LSTM, RNN, transformer, etc.)
Experience with Bedrock, JumpStart, HuggingFace
Experience evaluating ethical implications of AI and controlling for them (e.g., red-teaming)
Expertise in supervised learning and unsupervised learning along with experience in deep learning and transfer learning
Experience in generative algorithms (e.g., GAN, VAE, etc.) as well as pre-trained models (e.g., LLaMa, SAM, etc.)
Experience developing models from inception to deployment
Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools.
Experiment, evaluate, and create generative AI products for a variety of tasks, such as extracting data, summarizing documents, and other generative model applications.
Use fine tuning and advanced knowledge retrieval methods to improve the performance of generative AI models on specific tasks
Evaluate the performance of models and make necessary improvements
Collaborate with other scientists, data engineers, machine learning operations engineers, prompt engineers, and product owners to develop generative AI products
Engineer features by using your business acumen to find new ways to combine disparate internal and external data sources.
Share your passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods and standards.
Collaborate, coach, and learn with a growing team of experienced Data Scientists.
Stay connected with external sources of ideas through conferences and community engagements
Proven experience in developing and executing strategic recruitment plans aligned with organizational goals.
Excellent communication and interpersonal skills, with the ability to assess both technical and cultural fit during candidate evaluations.
Adept at building and maintaining strong relationships with hiring managers and cross-functional teams.
Manage the complete recruitment lifecycle, from sourcing and screening to onboarding, ensuring a seamless and positive candidate experience.
Cultivate strong relationships with hiring managers and collaborate effectively with internal stakeholders to understand and address departmental needs.
Lead and mentor a high-performing recruitment team, adjusting team size based on the nature and volume of hiring requirements.
Demonstrate a proactive approach by taking ownership of candidate engagement throughout the recruitment process.
Skillfully extract actionable insights from recruitment data while maintaining impeccable data hygiene, facilitating strategic refinement and informed decision-making.
Bachelors Degree in Data Science, Computer Science, or related field
7+ years of Data Science and Machine Learning experience required
Experience working on Insurance domain/Insurance / Health Payer (strongly preferred)
Proficiency with Machine Learning concepts and modeling techniques to solve problems such as clustering, classification, regression, anomaly detection, simulation and optimization problems on large scale data sets
Ability to implement ML best practices for the entire Data Science lifecycle
Exceptional communication and collaboration skills to understand business partner needs and deliver solutions
Bias for action, with the ability to deliver outstanding results through task prioritization and time management
Experience with data visualization tools — Tableau, R Shiny, etc. preferred
Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques and tools.
Effectively communicate the analytics approach and how it will meet and address objectives to business partners.
Advocate and educate on the value of data-driven decision making; focus on the “how and why” of solutioning.
Lead analytic approaches; integrate solutions collaboratively into applications and tools with data engineers, business leads, analysts and developers.
Create repeatable, interpretable, dynamic and scalable models that are seamlessly incorporated into analytic data products.
Engineer features by using your business acumen to find new ways to combine disparate internal and external data sources.
Share your passion for Data Science with the broader enterprise community; identify and develop long-term processes, frameworks, tools, methods and standards.
Collaborate, coach, and learn with a growing team of experienced Data Scientists.
Stay connected with external sources of ideas through conferences and community engagements.