Alex Chen

Spearheaded the launch of a new AI-powered anomaly detection platform, resulting in a 30% increase in customer adoption within 12 months and generating $5M+ in new recurring revenue.

Managed end-to-end product lifecycle for ML-driven predictive analytics tools, collaborating with Data Science and Engineering teams to reduce model inference latency by 25% and improve prediction accuracy by 15%.

Defined and prioritized product features based on market research, competitive analysis, and customer feedback, leading to a 20% improvement in user satisfaction scores for core AI functionalities.

Developed and executed go-to-market strategies, including pricing and positioning, for two major AI product releases, contributing to a 40% growth in product line revenue year-over-year.

Mentored a team of 3 junior product managers, fostering a data-driven product culture and improving team efficiency by implementing new agile sprint planning and review processes.

Launched an AI-driven personalized recommendation engine that increased user engagement by 18% and boosted conversion rates by 10% for key product categories.

Collaborated with data scientists to optimize existing ML models, reducing computational costs by 15% while maintaining model performance through feature engineering and model compression techniques.

Conducted extensive user research and A/B testing for new AI features, informing product iterations that led to a 5% uplift in user retention within the first 6 months post-launch.

Authored detailed product requirements documents (PRDs) and user stories for ML-centric features, ensuring clear communication and alignment across engineering, design, and marketing teams.

Managed a backlog of over 50 ML product features, prioritizing based on business impact and technical feasibility, resulting in the successful delivery of 90% of planned features on schedule.

Designed and implemented predictive models using Python (Scikit-learn, TensorFlow) for a healthcare client, improving disease prediction accuracy by 22% compared to baseline methods.

Engineered data pipelines and performed ETL processes on large datasets (1TB+) using SQL and Spark, ensuring data quality and readiness for machine learning applications.

Collaborated with product teams to translate business problems into data science solutions, contributing to a new fraud detection system that reduced false positives by 10%.

Presented complex analytical findings and model performance metrics to non-technical stakeholders, facilitating data-driven decision-making for product feature prioritization.

Developed and maintained documentation for ML models and data processes, ensuring reproducibility and adherence to best practices for a team of 5 data scientists.