Leads and executes deep dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision making. Mentors and develops junior data scientists and analysts.

QUALIFICATIONS

• MS/Ph.D. in Computer Science, Machine Learning, Operations Research, or a related field
• 3+ years of NLP implementation experience in the industry including experience building, scaling, and optimizing training and inferencing APIs for deep neural networks
• 5+ years of coding experience in languages such as Python, PySpark,
• Strong exposure to open source/commercial LLM’s through technologies such as Hugging Face, OpenAI, etc..
• Strong communication skills, with the ability to explain complex concepts to both technical and non-technical audiences.
• A deep understanding of the GenAI landscape, including recent advancements and emerging trends including the responsible use of GenAI models
• Experience working in cross-functional teams and a track record of balancing technical decisions with business objectives.

PREFERRED QUALIFICATIONS:

• Client facing/consulting experience
• Experience in IT
• Contributions to AI/ML community through publications, open source contributions, articles, etc..

• Lead AIML (including GenAI and Graph-ML) solutions development, prioritizing areas for exploration and improvement, and reviewing in-development models for quality as they are being trained.
• Prioritize AI features, models, and controls, engaging with product leaders and serving as AIML subject matter expert to drive tangible business outcomes
• Design and manage the model quality process, including both automated pipelines and manual review.
• Evaluate new technologies including vendor solutions to drive best practices in incorporating third-party technologies in a responsible manner
• Industry and Academic Awareness: Stay up-to-date with the latest developments in around AI and associated technologies in both academia and industry, integrating relevant advancements into our AIML strategy.
• Business partner alignment: Work closely with business partners, external partners, and other cross functional teams to align on AIML requirements and show sense of urgency in delivering insights.
• Technical Implementation: Translate research findings into practical solutions, exploring novel techniques, algorithms, and approaches that can be applied to real-world challenges. developing and optimizing AIML models and systems for real-world applications.
• Effective Communication: Communicate complex technical concepts clearly and concisely to both technical and non-technical stakeholders, fostering a culture of knowledge sharing and learning.
• Participate in special projects as required.