SummaryAs a Principal Engineer in Conversational Analytics, you will play a pivotal role in revolutionizing how Nordstrom leverages conversational data to enhance customer interactions. You will lead the design and implementation of advanced analytics solutions within the Enterprise Data Platform, driving data-driven decisions that improve customer experience and business outcomes.
Responsibilities- Architect scalable conversational analytics solutions within the Data Platform and BI tools.
- Lead the technical vision for conversational AI and NLP capabilities, ensuring reliability and cost-effectiveness.
- Drive technical excellence and establish best practices for MLOps, model deployment, and AI system monitoring.
- Collaborate with cross-functional teams to deliver impactful solutions.
- Design and implement data pipelines for processing conversational data from various sources.
- Mentor and develop senior engineers in AI, machine learning, and conversational analytics.
- Stay current with advances in conversational AI and NLP technologies.
- Ensure data quality and governance, implementing security and compliance measures.
- Lead architectural discussions and make key technical decisions.
- Integrate conversational analytics capabilities into the broader Enterprise Data Platform ecosystem.
Requirements- 8+ years of experience in software engineering with a focus on data engineering, machine learning, or AI systems.
- Bachelor's degree in Computer Science, Data Science, or related field; master's degree preferred.
- 3+ years of technical leadership experience in high-scale environments.
- Expertise in conversational AI and NLP, including large language models and sentiment analysis.
- Strong background in machine learning engineering and MLOps best practices.
- Experience with cloud platforms and big data technologies.
- Proven track record of building production-grade AI/ML systems.
- Experience with streaming data architectures and event-driven systems.
- Strong programming skills in Python, SQL, and familiarity with ML frameworks.
- Customer-focused mindset with ability to translate business requirements into technical solutions.
- Experience with data governance, privacy, and security.
- Ability to drive technical innovation while balancing business needs.
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