דרושים » ניהול ביניים » data Science

Were looking for an Applied Data Scientist to help us build and scale the core marketing science products that power our platform. This role sits at the intersection of data science and engineering: owning statistical models end to end, deploying them into production, and turning advanced analytics into reliable, customer‑facing capabilities. Your work will directly influence how thousands of brands measure ROI and allocate millions in marketing spend.
What Youll Do:
Build marketing measurement and optimization products (e.g., attribution models, MMM, incrementality testing, forecasting systems)
Develop and deploy statistical and machine learning models to production
Create scalable data pipelines and APIs that serve real-time analytics
Partner closely with Product, Engineering, and other stakeholders to translate business questions into clear analytical solutions.
Own features end to end from statistical design and validation through deployment, monitoring, and iteration.

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 Research Scientist .
As a Large Scale Video Understanding Research Scientist, you will play a key role in improving video generation quality and efficiency by improving video and audio understanding pipelines used for both training data construction and model evaluation.. This role demands hands-on work with large-scale Video Language Models (VLLMs), including fine-tuning, post-training, and control, alongside implementing classic computer vision and signal processing algorithms and applying strong research skills. Your expertise in post-training and controlling large scale foundational models, understanding statistics, implementing complex systems and eliminating bugs will be crucial, as our video training sets consist of petabytes of data processed across hundreds to thousands of virtual machines.
What you will be doing:
Fine-tune and control VLLMs for video and audio understanding.
Design algorithms for balancing, filtering, and curating training and evaluation datasets, informed by model behavior and failure modes.
Implement classic and modern algorithms for processing, clustering, evaluation and filtering of large scale datasets.
Work within high-performance, scalable distributed systems capable of handling petabytes of data, with attention to throughput, correctness, and reproducibility..
Collaborate with other researchers and product stakeholders to iteratively improve training sets and evaluation protocols through tight feedback loops driven by model performance.