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Develop predictive models, NLP and machine learning systems using large user datasets to improve search, matching, recommendations, and other digital products. Build scalable data products with Engineering and Product teams, applying statistical analysis, vector search, LLM/RAG techniques, embeddings, classifiers, and deep learning. Collaborate across teams to research new methods and deliver production-ready software.

Develop predictive models, NLP and machine learning systems using large user datasets to improve products and create new data products. Responsibilities include statistical analysis, scalable data system development, prompt engineering, large language model benchmarking, and agentic workflow experimentation. The role collaborates with Engineering, Product, BI, Legal, and Management teams and may involve production model deployment and backend development.

Administer and support Microsoft SQL Server databases across Azure and on-premise environments. Responsibilities include database architecture, performance tuning, migrations, maintenance, backups, recovery, security, integrity, high availability, disaster recovery, capacity planning, monitoring, and automation. The role also troubleshoots resource contention, blocking, deadlocks, and integrity issues while participating in incident, change, and problem management and coordinating technical solutions with stakeholders and architects.

Lead SDET responsible for translating technical/architectural designs into low-level test solutions, mentoring engineers, developing and owning test automation frameworks, creating API/backend tests, extending API automation, supporting CI, and conducting performance, security, and scalability testing.

Lead QA for AI-driven products, designing QA strategy and frameworks (including LLM-as-a-judge), automating test case generation, overseeing model evaluation, coordinating with product and engineering, and managing and mentoring QA teams to ensure high-quality releases.

Lead a small data science team to research and build NLP/ML solutions (LLMs, RAG, vector search) for search, ranking, and content features; collaborate with product and engineering, estimate and run projects, mentor data scientists, and implement prompt-engineering and agentic workflows to improve user experience.

Lead design and implementation of agentic LLM-powered automation, build RAG and memory-augmented systems, optimize prompt and model performance, use embeddings and vector DBs, create evaluation frameworks, and collaborate cross-functionally to ship production features.

Lead a data science team to design, develop, and deploy ML/NLP and LLM-based solutions (search, recommendations, content generation/moderation). Convert business requirements into scalable models and pipelines, validate models, collaborate cross-functionally, and recruit and mentor data scientists.

Validate BI solutions by building test strategies, plans, and cases for ETL and BI components. Verify SQL queries, validate data sources and transformations, execute manual and automated tests, validate dashboards/reports, and produce test summary reports while collaborating with BI developers and business analysts.

Lead mobile product vision and roadmap, prioritize and execute app features, drive cross-functional alignment, integrate AI/LLM capabilities, define KPIs, run A/B experiments, and deliver personalized mobile experiences that improve user conversion and retention.