Senior Machine Learning Engineer - Embedded Insights

Plaid•New York, NY

12d•$228,960 - $315,360

About The Position

The Embedded Insights team supports Plaid’s mission to build a world-class suite of intelligence products. We identify the best opportunities to use machine learning in Plaid products, prove out those opportunities, and collaborate with cross-functional partners to turn them into real-world production systems. As a Senior Machine Learning Engineer on Embedded Insights, you will help shape Plaid’s future by building machine learning-powered products and features. You will initially support the Plaid App, working on a 0-to-1 consumer-facing product and helping establish product-market fit for a new business line. You will work closely with product managers, data scientists, engineers, customers, and other machine learning engineers to translate ambiguous opportunities into effective ML systems that create measurable customer value. In supporting the Plaid App, you will: Build machine learning-based features for a 0-to-1 consumer-facing product. Partner with product managers to translate ambiguous business requirements into machine learning problems and influence product strategy and roadmap decisions. Rapidly iterate and experiment to help drive product-market fit for a new business line. Work with Data Scientists to define success metrics and guardrails for new machine learning features. Partner with MLEs across product areas to build effective data feedback loops. As a member of the broader Embedded Insights team, you will: Analyze Plaid’s unique datasets to identify high-impact opportunities for machine learning and complete proofs of concept to validate them. Embed with product teams and work closely with product and engineering partners to productionize models and deploy them in real-world, customer-facing products. Optimize and maintain the health of existing models by developing new features, identifying effective retraining cadences, and creating metrics, alerts, and dashboards to monitor performance. Communicate technical decisions, tradeoffs, and system behavior clearly to both technical and non-technical partners.

Requirements

6+ years of experience in machine learning, including deploying models into real-world, customer-facing systems.
High agency and creativity, with experience identifying, defining, and proposing high-impact machine learning opportunities.
Ability to analyze large and complex financial datasets and derive actionable insights.
Experience taking machine learning systems from experimentation or proof of concept through production and ongoing improvement.
Proficiency in SQL and Python, as well as data visualization and analysis tools.
Ability to clearly communicate complex technical systems, decisions, tradeoffs, and outcomes to cross-functional partners.
Advanced degree or equivalent work experience in Statistics, Economics, Mathematics, Data Science, or a related field.

Responsibilities

Build machine learning-based features for a 0-to-1 consumer-facing product.
Partner with product managers to translate ambiguous business requirements into machine learning problems and influence product strategy and roadmap decisions.
Rapidly iterate and experiment to help drive product-market fit for a new business line.
Work with Data Scientists to define success metrics and guardrails for new machine learning features.
Partner with MLEs across product areas to build effective data feedback loops.
Analyze Plaid’s unique datasets to identify high-impact opportunities for machine learning and complete proofs of concept to validate them.
Embed with product teams and work closely with product and engineering partners to productionize models and deploy them in real-world, customer-facing products.
Optimize and maintain the health of existing models by developing new features, identifying effective retraining cadences, and creating metrics, alerts, and dashboards to monitor performance.
Communicate technical decisions, tradeoffs, and system behavior clearly to both technical and non-technical partners.