Senior Data Scientist - Embedded Insights

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.

About the Team

At Embedded Insights, we find the best machine learning opportunities for external products and internal systems, and collaborate with cross-functional partners to bring them to life.

We are a central team of Machine Learning Engineers and Data Scientists. We embed with partner teams to build and apply machine learning models that improve internal decision-making and power the Plaid product suite.

About the Role

You will be the first Data Scientist on the Embedded Insights team, part of Plaid’s Data organization. You will establish the analytics and metrics backbone for a team supporting a diverse set of internal and external products.

You will help drive better decision-making, support machine learning model development, and contribute directly to the health of the Plaid network and the quality of Plaid’s products.

Your day-to-day work will include:

• Analyzing entities across the Plaid network to understand behavior and identify opportunities, anomalies, and risks.

• Creating foundational metrics, dashboards, and monitoring systems that provide a clear view of network health and machine learning model performance.

• Evaluating the value and performance of machine learning models using customer and internal data.

• Identifying opportunities to improve existing models and translating findings into clear, actionable narratives for product and engineering leaders.

• Designing experiments, defining success metrics and guardrails, analyzing results, and communicating recommendations to stakeholders.

• Analyzing Plaid product and customer data to identify opportunities for product improvement and expansion.

• Partnering with cross-functional teams to build reliable data models and analytics workflows.

What Excites You

• Applying quantitative analysis, data mining, and data visualization to help keep the Plaid network healthy and improve our product suite.

• Informing and influencing product and engineering teams through rigorous analysis, thoughtful presentations, and clear recommendations.

• Turning ambiguous business questions into structured analytical approaches and measurable outcomes.

• Helping shape long-term data science and machine learning roadmaps, including how teams iterate, evaluate, and make decisions.

• Establishing analytics practices and frameworks from the ground up as the team’s first Data Scientist.

• Championing a data-first approach to decision-making across Plaid.

What Excites Us

• 6+ years of industry experience in Data Science or a related analytics role.

• Deep familiarity with SQL and data visualization tools.

• Understanding of modern machine learning techniques, such as classification, clustering, and optimization.

• Experience evaluating model performance, identifying opportunities for improvement, and connecting technical results to business outcomes.

• Proven ability to tailor analytical solutions to business problems while working with cross-functional partners.

• Ability to code and iterate independently in Python, particularly for exploratory data analysis.

• Strong written and verbal communication skills, including the ability to explain analytical methods, tradeoffs, and recommendations to technical and non-technical audiences.

• Experience building or partnering on data pipelines using dbt or Airflow is a plus.

• Bachelor’s degree or equivalent work experience in Computer Science, Statistics, Engineering, Economics, or a closely related field.

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!

Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.

Please review our Candidate Privacy Notice here.

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.