Data Scientist

Deuna•New York, NY

1d•Remote

About The Position

DEUNA is a rapidly growing startup that simplifies global payments and powers next-generation commerce through a single platform. Its products consolidate payment solutions, provide payment orchestration, and centralize payment reconciliation. DEUNA operates across LATAM. The Data Scientist is a hands-on technical leader who will own machine learning and experimentation for DEUNA’s payments platform. The role builds the foundations of the data science practice and independently designs, trains, and validates models that increase conversion and reduce declines and fraud in real-time payment flows across merchants and payment providers. The position is part of the technology organization and collaborates with project leads responsible for customer AI and modeling solutions, as well as data engineering and BI teams that manage data foundations and inference services. The role may grow into people leadership as DEUNA’s AI efforts expand, depending on the candidate’s skills.

Requirements

At least 5 years of experience applying machine learning to real production problems.
Hands-on expertise in feature generation, model selection, training, and hyperparameter optimization.
Experience designing and running experiments, including A/B testing and experimentation platforms, and validating model impact.
Proficiency in Python and SQL.
Comfort querying large datasets in tools such as Snowflake.
Familiarity with TensorFlow and Airflow.
Ability to explain model management and rollout experience.
Self-directed and able to move work forward without close supervision.
Clear communication with technical and non-technical audiences, including setting realistic expectations with leadership about model capabilities.
Experience guiding technical infrastructure teams to implement features that enable efficient data science and ML training pipelines and systems.

Nice To Haves

Experience in payments, fintech, fraud, or other high-volume transactional environments.

Responsibilities

Own the end-to-end modeling lifecycle for payment optimization use cases, including analyzing merchant and transaction data, defining modeling strategy, generating features, training and tuning models, and releasing models through existing inference infrastructure.
Build an experimentation framework to measure model performance in collaboration with systems and infrastructure engineers, tracking results from early signals through month-over-month outcomes.
Connect model performance to commitments in merchant contracts and the product pipeline.
Partner with data teams to define and track data processing pipelines that surface insights and shape DEUNA’s offerings.
Use experiments and rapid model iteration to tailor approaches to merchant data and goals.
Work with existing models while developing new models.
Guide technical infrastructure teams in implementing features that support efficient data science and machine learning training pipelines and systems.