Senior Applied Scientist

Required Qualifications

• Bachelor's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)

• OR Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)

• OR Doctorate in Computer Science, Electrical or Computer Engineering, or related field AND 1+ years related experience (e.g., statistics, predictive analytics, research)

• OR equivalent experience.
• 1+ years of experience with generative AI OR LLM/ML algorithms

• Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines

• Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow)

• 3+ years of experience publishing in peer-reviewed venues or filing patents .

• Experience presenting at conferences or industry events

• 1+ year of experience developing and deploying live production systems

• Experience across the product lifecycle from ideation to shipping

bility to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

#BICJobs

• Deliver impactful solutions by executing high‑leverage data science and analytics initiatives within a product area or feature team, ensuring measurable improvements to user and business outcomes.

• Lead the design and implementation of advanced model fine‑tuning pipelines, including Reinforcement Learning from Human Feedback (RLHF), to align AI system behavior with user intent and improve performance in real‑world scenarios.

• Own complex, end‑to‑end projects that combine technical depth with cross‑functional collaboration, influencing feature direction and prioritization rather than broad organizational investment decisions.

• Foster alignment and trust across partner teams through clear, actionable communication and collaborative problem‑solving.

• Develop and maintain robust measurement systems, experimentation frameworks, and causal inference methodologies tailored to dynamic AI systems and enterprise‑scale environments.

• Mentor and support peers by sharing best practices, reviewing designs, and contributing to a collaborative, high‑performance team culture.

• Leverage AI to streamline workflows and enhance team productivity through intelligent automation and innovation.