לוח משרות דרושים Data Scientist עבודה היברידית, לחיפוש זה נמצאו 10 משרות

we are looking for a AI Applied Scientist - Insights & Recommendations.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.

We're looking for an Applied Data Scientist to own the data science strategy behind this initiative.
This is a high-ownership, senior role sitting at the intersection of product, data science, and capital markets. You'll build statistical and ML models end to end - from conception through production - and turn complex financial-product questions into reliable, customer-facing decisioning systems. You'll work across Product, Engineering, Finance, Operations, Legal, and external capital partners to define how evaluates financial-product opportunities, manages risk, measures performance, and scales responsibly.
The right person brings strong modeling depth, sharp product judgment, and the business context to work in ambiguity. Fintech, lending, credit, or risk experience is strongly preferred - but we're also open to senior data scientists from adjacent domains who've built high-stakes models in complex, data-rich environments.
What Youll Do:
Own the end-to-end data science strategy for financial products - from roadmap and model development to production deployment and governance.
Build models and decisioning frameworks across the full lending lifecycle: merchant eligibility, underwriting, risk assessment, repayment behavior, and growth forecasting.
Translate ecommerce, revenue, marketing, and operational data into actionable signals that power financial-product decisions and partner evaluations.
Partner with Product, Engineering, Finance, Legal, and Operations to turn analytical work into scalable data products, APIs, internal tools, and explainable recommendations that hold up in commercial and regulatory contexts.
Design measurement frameworks that span funnel performance, repayment outcomes, customer adoption, partner performance, and long-term unit economics.
Establish model validation, monitoring, and governance practices appropriate for high-stakes financial decisioning.
Define the long-term fintech data science roadmap - what to build internally, what to partner on, and where modeling can create durable competitive advantages.

we are looking for a Fraud Data Scientist.
As a Fraud Data Scientist, you will be at the front lines of protecting our ecosystem from sophisticated financial fraud and abuse. You will join a high-impact team operating in a data-rich, high-frequency environment where seconds matter.
In this role, you will take ownership of the end-to-end machine learning lifecycle-from uncovering complex fraud patterns to deploying highly scalable, real-time models into production. You will collaborate closely with Engineering, Product, and Risk Operations to build robust defenses that balance strict security with a seamless user experience.
What You Will Be Doing
Model Development & Deployment: Design, train, and deploy advanced machine learning models (e.g., gradient boosting, anomaly detection, graph networks) to detect and mitigate fraud in real-time.
Production Ownership: Take full ownership of putting models into production systems, ensuring low-latency execution and high reliability.
Agentic Workflows: Research, build, and implement Agentic flows and LLM-driven orchestration to automate multi-step fraud decisioning, logic routing, and investigation paths.
Adversarial Analysis: Conduct deep-dive exploratory analysis on massive datasets to identify emerging fraud vectors, loops, and coordinated attacks.
Feature Engineering: Build and optimize real-time streaming and batch features to improve model signal and precision.
Experimentation & Monitoring: Design rigorous shadow-testing and A/B testing frameworks for new models. Set up continuous monitoring pipelines to catch data drift and performance degradation early.

we are looking for a Junior Fraud Data Scientist.
As a Junior Fraud Data Scientist, you will contribute to our ongoing efforts to protect our ecosystem from financial threats and abuse. Working under the guidance of senior team members in a data-rich environment, you will assist in identifying malicious behaviors, support detection coverage, and help maintain our automated mitigation strategies.
This role is designed for an analytical professional with 1-2 years of experience who wants to build a deep expertise in adversarial data, learn from an experienced team, and develop their technical skills across modern cloud data platforms.
What You Will Be Doing
Exploratory Data Analysis: Support the team by mining behavioral and transactional datasets to help identify anomalies and emerging fraud patterns.
Model Support & Optimization: Assist in building, tuning, and validating machine learning models (e.g., XGBoost, LightGBM) under the supervision of senior data scientists.
Feature Generation: Extract, engineer, and prepare new data features from structured and unstructured sources to help improve model performance.
Dashboarding & Monitoring: Build and maintain internal dashboards and pipelines to track model health, data drift, and key fraud KPIs.
Cross-functional Collaboration: Work alongside Fraud Analytics and Product teams to help translate operational fraud insights into automated data solutions.