דרושים » הנדסה » Applied Data Scientist

Were looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
Youll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. Youll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
Develop and improve our core algorithms.
Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices-not just standalone prototypes.
Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.

We are looking for a passionate Senior Data Scientist to join our all-star team. This is an amazing opportunity to join a multi-disciplinary A-team while working in a fast-paced, results, marketing and data-oriented environment. If you are experienced but still hungry to learn and impact - wed love to have you on our team!



What Youll Do

Devise, initiate, manage, and execute cutting-edge exploration and exploitation projects.
Apply your knowledge of Data Science, Machine Learning, and AI-driven algorithms to create scalable data solutions.
Analyze extremely large (petabytes) and diverse datasets to solve complex problems, and develop automated, intelligent systems to address them.
Design, build, and deploy AI/ML models (including predictive models, recommendation systems, and generative AI solutions) to drive business impact.
Leverage modern AI tools and frameworks to accelerate research and production workflows.
Proactively seek growth opportunities aligned with core KPIs.
Enable real-time and near real-time data insights - instant data gratification guaranteed.

We are looking for an experienced, independent team player who has great data & computer science skills, a passion for data, and excellent analytical and algorithmic skills.

This role is diverse, encompassing AI/ML, data analysis, and backend engineering.

You will play a crucial role within a mission-critical team responsible for managing the heart and brain of Sunbits primary products. This involves working on core systems, such as POS underwriting models, models for merchant operations, Fraud investigator AI agents and more.

You will participate in cutting-edge risk management systems that safeguard the loan product's financial operation, as well as operational systems, with a direct impact on its financial success.

Our AI/ML team is part of the R&D group, so you will work closely with engineers and product managers as well.

Key Responsibilities
Research and develop statistical behaviors, study domain-specific data.
Develop state-of-the-art machine learning models end to end, including development, deployment, and continuous improvement. Both in-weight learning and in-context learning, for risk / fraud / operations related projects. This includes integrating models into production services and ensuring compliance with regulatory processes (e.g. providing evidence for production model audits).
Operate backend infrastructure for training and deploying ML models, ensuring optimal performance and reliability.
Develop and maintain Python code for translating ML model outputs into financial decisions.
Analyze 15+ different data sources in order to train models and agents to catch fraudulent patterns.
Conduct analytical research on our models impact on the portfolio.
Strategize and implement changes to enhance portfolio performance.

In this role, you will operate as a company-level expert in product analytics, partnering with Product, Engineering, and cross-functional leadership to translate complex, large-scale behavioral data into actionable insights that shape the experiences of billions of people worldwide. You will lead the design of rigorous analytical frameworks, advance forecasting and predictive modeling capabilities, and influence the highest-priority product investments through data-driven storytelling and strategic recommendations.
Data Scientist, Product Analytics Responsibilities
Lead the design and execution of complex analytical projects across product areas, applying advanced statistical methods, causal inference, and experimentation to evaluate product impact at scale
Develop and maintain sophisticated predictive models and forecasting frameworks to inform product roadmaps, goal setting, and resource prioritization
Define and evolve success metrics and measurement strategies for product initiatives, ensuring goals accurately reflect user and business value
Identify and size high-impact product opportunities by synthesizing behavioral data, qualitative signals, and business context across multiple data sources
Partner with Product and Engineering teams to design and analyze large-scale A/B tests and feature rollouts, translating results into clear recommendations for product direction
Build scalable, self-service data pipelines and visualization interfaces that enable cross-functional teams to explore and act on product performance data independently
Communicate complex analytical findings to diverse audiences including product leadership and executives through concise, compelling data narratives and presentations
Contribute to functional data science strategy by proactively identifying methodological gaps, championing analytical best practices, and setting goals across teams
Mentor other data scientists on analytical design, hypothesis formulation, and quantitative modeling, elevating the overall quality and rigor of the team's work
Collaborate with Data Engineering and Research partners to improve data infrastructure, eliminate bias in data collection, and ensure analytical outputs accurately represent the populations of interest

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.

Were looking for a Data Scientist to join our R&D department!
Why is this role so important?
A data-focused company, and our unique AI and machine learning capabilities are the center of our business.
As part of this role, you will create data models that help analyze the petabytes of data we receive from various sources, and research and develop new features and capabilities for our product solutions
As a data scientist, you will work on the very core of the company. Part of your role will be to turn raw data into usable metrics and develop new models and statistical algorithms to support out-of-the box requests from customers who want custom digital insights.
So, what will you be doing all day?
Your daily responsibilities may include:
Working on versatile use cases within a dynamic, fast paced production environment
Strategic thinking and problem solving, with a strong business sense
Analyzing data from various sources, and turning it into usable input
Understanding various use cases from different customers, and creating models that can translate these cases into meaningful insights
Initiating newly defined tasks from exploration to proof of concept
Owning the finalization of defined tasks (from POC to deployment in production)
Collaborating with data engineers to make sure we reach the desired production results
Owning the ongoing validation process for existing metrics
Collaborating and brainstorming best practices with other teams within the R&D department
Deploying machine-learning models into production for an ongoing use.

We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.

The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions - like demand forecasts - to drive revenue management decisions that actually move the needle.

At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through an optimization engine that simulates the market - weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies - to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.

You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.

Responsibilities:

Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Design, build, and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

Were looking for a sharp and driven Product Data Scientist to join our fast - paced Data Labs team.
As part of a high-performing group of analysts, youll handle complex, custom requests from some of the biggest names in the market, turning raw data into strategic, ad hoc insights.
The work is intense and constantly evolving - it requires strong analytical skills and the ability to work independently under pressure.
Youll need to be curious, adaptable, and willing to dive deep to find answers where others might stop. If you thrive in a demanding environment and are excited by the opportunity to make a real impact, we want you on our team.
What does the day-to-day of a Product Data Science Customer Facing (Data Labs) look like:
Building custom-made reports end to end - from meeting with the client and understanding their needs, to writing the code and setting up the report for ongoing delivery.
Apply your expertise in quantitative analysis and data mining to turn data into insights.
Conduct research and develop tools that will help answer clients business questions by using our raw data sets and algorithms.
Partner with Engineering teams to establish an infrastructure to scale solutions.
Partner with the Advisory Services team of Consultants to answer strategic business questions and deliver custom data products seamlessly.

We are looking for an experienced Analytics Engineer to join our Clinical Value team and develop the data foundations, infrastructure, and analytical tools that enable our work.
This hybrid role combines Analytics Engineering with hands-on data analysis. You will translate complex analytical and clinical needs into scalable, reliable, and reusable data solutions.
You will design data models, pipelines, analytical layers, and tools that enable complex Clinical Value studies across products and customers, while remaining hands-on with selected analytical projects.
You are expected to be independent, proactive, and impact-oriented, taking end-to-end ownership of technical challenges and opportunities for improvement.
evaluation.
Responsibilities:
Design, build, and maintain the data infrastructure supporting Clinical Value analyses, including reusable data models, pipelines, analytical layers, and tools.
Own the technical and analytical implementation of complex Clinical Value applications, adapting established methodologies to different products, customers, and real-world data environments.
Develop complex analytical solutions into reusable and scalable frameworks, enabling broader and more efficient execution across the team.
Identify data-quality issues, technical bottlenecks, and repetitive processes, and develop solutions and automation to improve reliability and efficiency.
Work closely with analysts, Data Engineering, and other technical teams to translate analytical needs into effective, maintainable data solutions and establish best practices.
Lead selected hands-on data-analysis projects, analyzing complex clinical and operational datasets to generate credible, actionable conclusions.
Collaborate with Go-to-Market teams, including Customer Success, to understand customer needs and support impactful customer-facing value demonstrations.

Were looking for a Senior Data Scientist to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

In this role, you will design, build, and productionize advanced models that help organizations secure AI systems across data, models, and runtime usage. Youll work closely with Product, Engineering, and Security teams to turn complex, real-world security problems into scalable, high-impact AI solutions.

This is a high-impact, hands-on role with true ownership, where your work directly shapes the intelligence powering a category-defining AI Security platform.


What Youll Do:

Own End-to-End Model Development (Research → Production)

Take full ownership of the model lifecycle: problem definition, research, prototyping, training, evaluation, deployment, and ongoing optimization.

Design and build scalable ML / AI solutions to detect, analyze, and mitigate AI-related security risks.

Ensure models are robust, explainable, and production-ready in real-world enterprise environments.

Partner Deeply with Product & Engineering

Collaborate closely with Product Managers to translate customer pain points and security requirements into clear modeling objectives.

Work hands-on with engineering to integrate models into production systems and data pipelines.

Balance research depth with pragmatic execution and business impact.

Build AI Security Intelligence

Develop capabilities across areas such as AI behavior analysis, anomaly detection, model governance, data risk detection, and usage observability.

Work with structured and unstructured data, including logs, prompts, model outputs, and metadata.

Continuously iterate on models based on usage patterns, feedback, and emerging threat vectors.

Lead Research POCs & Innovation

Stay at the forefront of LLMs, applied ML, and AI security techniques and bring new ideas into the product.