Macpower Digital Assets Edge
Principal Data Science Engineer
Macpower Digital Assets Edge - Dallas
Work at Macpower Digital Assets Edge
Overview
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Overview
Benefits: Medical, Dental, Vision, Paid time off, Stock plan, Remote work policy.
Role Overview:
Role Overview:
- Seeking a high-impact principal data science engineer to join a founding team focused on advancing our mission to combat the skilled worker crisis by unlocking high pay, high dignity careers for talent. The role will have a high degree of autonomy and will have the opportunity to touch all areas of the stack (full stack machine learning, Client operations and API development).
- This role represents a chance to join an early-stage team with a focus on creating large-scale positive impact in the world while also generating substantial value in a multi-trillion-dollar industry .
- Developing our core technology product, a data-rich system of engagement and record for identifying, qualifying, staffing, optimizing, and retaining skilled talent .
- Partnering to build and execute a data strategy in support of the long-term business vision .
- Building Client algorithms to support skilled worker retention as well as matching algorithms underlying a labor marketplace.
- Flexing across the entire machine learning model development lifecycle, including EDA, data pipelines, feature engineering, model productionisation and monitoring.
- nalyzing data (market, user behavior, etc.) to inform new feature development and the associated ROI across our various customer personas .
- Ensuring security best-practices around PII and other sensitive data/access .
- Bringing values-centered leadership and deep belief in our mission for serving customers, partners, & employees.
- Tracker record of building and shipping production-grade data products, from 0-1.
- Comfortable working across the stack with modern programming languages and frameworks ( Python, Java ) & platforms ( WS, GCP, Azure , etc.).
- ble to flex from 100% data science focus to 100% engineering execution based on company needs, with the most likely split being near the center.
- Early-stage startup experience.
- Big tech company experience at companies such as: mazon (strongly preferred), pple, Twitter, Amazon, Google, Meta, MicroSoft, Netflix , etc.