Fraud Data Scientist - ML & Product Analytics

Talanto; New York, Northern (NY, KY); Hybrid; USD 108,000 - 221,000; Full time

Benefits offered by this job

Medical, dental, vision, and 401(k)

Job summary

Talanto is seeking a Data Scientist to join the Fraud Data team. The role combines product analytics, machine learning, and fraud/risk insights to improve product performance across customer segments. You will own metrics, dashboards, and experiments that inform strategy and decision making.

You’ll design scalable data models, run backtests to evaluate model strategies, and collaborate with Product and Engineering on experiments for new customer‑facing features.

Qualifications

• 3–5 years of relevant experience, including at least 2–3 years working with product analytics, experimentation, or data‑driven products.
• Strong proficiency in SQL and Python, with experience analyzing complex datasets and translating insights into action.
• Hands‑on experience with product analytics, experimentation, and/or backtesting methodologies.
• Experience building and maintaining dashboards, reporting frameworks, and core product metrics.
• Strong communication and stakeholder management skills, with the ability to translate complex analyses into clear, actionable insights for technical and non‑technical audiences.

Responsibilities

• Work at the intersection of product analytics, machine learning, and fraud and risk to uncover insights that improve product performance.
• Own the metrics, dashboards, and experiments that inform product strategy and decision‑making.

Skills

SQL

Python

Product analytics

Experimentation

Dashboards

Stakeholder management

Job description

Talanto is seeking a Data Scientist to join the Fraud Data team. The role combines product analytics, machine learning, and fraud/risk insights to improve product performance across customer segments. You will own metrics, dashboards, and experiments that inform strategy and decision making.

You’ll design scalable data models, run backtests to evaluate model strategies, and collaborate with Product and Engineering on experiments for new customer‑facing features.