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- Explore Complex Data: Dive into massive, messy datasets—text, numeric, and behavioral—to surface hidden emotional patterns and social dynamics.
- Rapid Prototyping: Develop ML-powered features like anomaly detection, predictive scoring, and time series analysis—moving from concept to prototype in days, not months.
- Collaborative Development: Partner with product, design, and clinical teams to translate insights into compelling, user-facing experiences.
- End-to-End Model Deployment: Work closely with backend engineers to deploy, monitor, and continuously improve your models in a real-time, low-latency prediction environment.
- Champion Data Culture: Foster a culture of data excellence through dashboards, reproducible notebooks, and constructive, humble code reviews.
- Own the Challenge: You can take an ambiguous problem, chart a clear path forward, and execute independently when needed.
- Have a “Can-Do” Attitude: Obstacles are just opportunities for creative problem-solving.
- Find Joy in Data Discovery: You thrive on uncovering meaningful patterns in messy, real-world data— where others see chaos, you find structure, and you love communicating those insights through clear visuals or models.
- Are Fluent in ML: You know when a simple regression is best, when to reach for deep learning, and can explain your choices with clarity.
- 3+ years of professional experience as a Data Scientist, ML Engineer, or Data Engineer.
- Proven experience building and deploying machine learning models at scale (batch and real-time).
- Skilled at wrangling unstructured data and engineering features for behavioral analysis.
- Excellent written and verbal communication skills—you can translate complex math into product impact for a cross-functional team.
- Advanced SQL skills and hands-on experience with Elasticsearch for text or log analytics.
- Previous experience working with Silicon Valley companies or early-stage startups.