Machine Learning Engineer

Company: Carta
Location: San Francisco, California, United States
Type: Full-time
Posted: 04.JUN.2021
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Summary

The Company You'll Join At Carta we create owners and make private markets liquid. We live in a world where some people live on the equity...

Description

The Company You'll Join

At Carta we create owners and make private markets liquid.

We live in a world where some people live on the equity stack and enjoy exponential wealth growth and preferential tax treatment; others live on the debt stack and may work their entire lives for a company and retire only with the cash they've managed to save from their paychecks. Our contribution to solving the wealth inequality problem is moving people from the debt stack (payroll) to the equity stack. By making it as easy to issue equity to employees as it is to put them on payroll, we can create more owners.

At Carta, we are helpful, transparent, fair, and kind. We are relentless executors, unconventional thinkers, and masters of our craft.

To learn more, here is what one of our investors wrote about leading our Series F.

The Team You'll Work With

Our mission is to enable data-driven decisions and products across Carta by collecting accurate data, building scalable infrastructure and delivering advanced analytics. This is a foundational role in Carta's fast-growing Data Organization, working on one of the world's most valuable data sets at one of the fastest-growing FinTech companies of all time. The team consists of experts in product analytics, machine learning and data engineering. We partner with each other and Cartan's across the company to solve impactful problems. Our team strongly believes that being helpful accelerates results and we support one another to be successful at Carta.

The Problems You'll Solve

Examples Of Responsibilities Will Include

As a ML Engineer at Carta, you'll spend a third of your time partnering with domain experts across the company to analyze and explore Carta's proprietary data set, a third building models to power new products and accelerate Carta's business, and the last third developing Carta's machine-learning infrastructure.

  • Perform exploratory analyses to understand the dynamics of private markets and ownership
  • Develop machine learning models to power new financial products and to extract trends from performance of existing products
  • Automate monitoring of data distributions to detect and flag anomalies
  • Build and deploy containerized prediction services and integrate them with our data pipelines
  • Partner with product managers, engineers, and business teams to incorporate data-driven insights into decision-making
  • Own, coordinate, and solve complex, cross-functional problems that extend beyond the traditional boundaries of product, analytics, and data science
The Impact You'll Have

You will own significant projects directly aligning with Carta's company-wide initiatives of data products and data quality. Your work will empower leaders across the company to make good product decisions and optimize operational efficiency. Additionally, you will have the opportunity to set best practices for integrating our ML models into production helping Carta's current and future ML engineers.

About You

Candidates must have a strong foundation in statistics, be proficient in SQL and Python (or R), and have an analytical mindset. You have a strong bias towards simplicity, are excited by "zero to one" projects, and can efficiently communicate findings to leadership. Example traits that we value:
  • 2+ years of industry experience solving complex data problems with descriptive and predictive statistical models
  • Proficiency with modern programming languages (Python, R, SQL, etc.) and datastores (Redshift or similar)
  • A deep understanding of modern statistical and machine learning models, when to apply them, and how to evaluate their performance
  • Strong written and verbal communication skills, with a particular emphasis on data visualization
  • A collaborative attitude and a helpful personality
Nice to Have:
  • Experience with containerizing ML models and integrating them into production workflows
  • Experience with building simple ETL pipelines (Airflow or similar) to schedule model training / predictions

 
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