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If you love solving puzzles inside messy, real-world datasets, you'll feel at home here.
What You'll Work On
- Build, train, and refine machine learning and deep learning models using time-series, sensor, and behavioral data.
- Integrate data from wearables, fitness tracking platforms, and device APIs to create a clear story from movement, patterns, and activity signals.
- Develop and maintain data pipelines that support both batch and real-time analytics.
- Own model deployment in production environments — your models won't live in notebooks; they'll live in the world.
- Work closely with engineering teams to integrate ML models into mobile and web apps.
- Support logic for fraud, spoofing, and anomaly detection, ensuring data reflects real human activity.
- Make complex outputs easy to understand — not just for engineers, but for product and business users too.
You'll Thrive Here If You Have
- 5+ years of hands-on experience as an ML Engineer or Applied Scientist.
- Strong foundation in machine learning, deep learning, and time-series analysis.
- Experience working with wearables, IoT data, or sensor-based datasets.
- Fluency in Python, PyTorch or TensorFlow, and good software engineering habits.
- Experience building and shipping production ML systems using modern MLOps practices.
- Comfort with Node.js, APIs, and backend integration workflows.
- Understanding of data privacy, cloud ML infrastructure (AWS, GCP, or Azure), and edge inference.
- A solid grasp of feature engineering, statistical reasoning, and evaluating what "good" looks like in a model.
- You enjoy going deep and figuring things out.
- You care about clarity — in your code, in your thinking, in how you explain your work.
- You see data not just as numbers but as stories about real people.
- You value responsibility. When something is yours, you own it end-to-end
Key Skills
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