לוח משרות דרושים Data Scientist משרה מלאה, לחיפוש זה נמצאו 90 משרות

Were looking for a sharp and driven Data Analyst 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 Big Data Analyst 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 which will help answer client's business. questions by using raw data sets and algorithms.
Partner with Engineering teams to establish an infrastructure to scale solutions.
Partner with Advisory Services team of Consultants to answer strategic business questions and deliver custom data products seamlessly

Were looking for a Data Scientist to join our R&D department!


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

1. AI Architecture & Technical Leadership

Guide AI architectural direction across platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
Act as a senior technical reviewer and thought partner for complex or cross-team AI design decisions.
Provide technical oversight on areas such as exploring new models/technologies/opportunities for the company
Surface architectural risks, tradeoffs, and long-term implications, including cost, security and compliance aspects and clearly advise the VP of AI when certain technical directions should not be pursued.
This role influences judgment, clarity, and experience, not through blocking authority.

2. Hands-on Applied Innovation (Core Pillar | ~50%)

Spend at least 50% of time hands-on, building:
End-to-end AI prototypes
Technical demos and proofs of concept
Exploratory implementations of new AI capabilities
Drive applied innovation that:

De-risks new technologies
Demonstrates feasibility and impact
Informs product direction and business opportunities
Build fast, concrete examples that teams can learn from and extend.
Transition successful prototypes to team ownership for further development and scaling.
This role is expected to lead AI innovation by doing, while working closely with product, medical and engineering

3. Best Practices & Technical Enablement

Define and promote best practices for applied AI development, including:
Rapid prototyping and vibe coding.
Agent design, orchestration, and evaluation patterns
Experimentation, benchmarking, and validation workflows
Help teams align on shared technical patterns, tools, and standards.
Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
Lead technical deep dives, architecture discussions, and design reviews.
4. AI Compliance & Regulatory Enablement (Technical Scope)

Ensure Navinas AI development practices align with applicable AI regulations for a software product handling sensitive medical data.
Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation expectations.
Support and contribute to AI-related compliance and regulatory documentation, in close collaboration with Legal, Security, and Medical Research teams.
Serve as a technical point of reference for AI compliance questions.

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 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.

We are rapidly growing into new verticals, and we are seeking a visionary Developer Relations Senior Manager to broaden our impact. In this regional leadership role, you will champion Israels growing AI ecosystem, managing a high-performing team of Developer Relations Managers.

You will lead technical projects in GenAI/ML application development, consumer internet, and embedded AI systems. Working at the crossroad of engineering, strategy, and community, you will promote our hardware and SDK offerings. This role supports faster adoption of GPU-accelerated technologies by developers, startups, and ISVs. If you are a technically fierce, highly autonomous leader who thrives in a matrixed environment and wants to build the future of AI, let's talk.

What Youll Be Doing:

Lead & Mentor: Guide, collaborate and encourage a regional team of Developer Relations Managers covering Israel's dynamic AI ecosystem.

Drive Technical Strategy: Architect and complete a comprehensive developer adoption strategy for key industries, ensuring our platforms remain the gold standard.

Build Technical Alliances: Establish deep, positive relationships with CTOs, engineering leads, startups, and ISVs within the local ecosystem.

Evangelize & Accelerate: Act as the ultimate technical mentor and focal point for the developer community. Help developers unlock the full value of our SDKs and hardware to solve complex, real-world problems.

Champion the Community: Lead our presence at targeted developer events, hackathons, and industry activities.

Bridge the Matrix: Collaborate across internal product divisions, sales geographies, solution architects, and marketing teams to align our local ecosystem strategy with global value messaging.

As a Data Science Manager, you will lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions. You will own the end-to-end development lifecycle of AI projects, from exploratory research and experimentation through to scalable deployment and optimization. In this role, you will partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery. You will also drive innovation across a broad spectrum of ML domains, including supervised/unsupervised learning, anomaly detection, and generative AI, while providing hands-on guidance and championing best practices in MLOps, data governance, and model evaluation.
Key Responsibilities
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.

We are seeking a Senior NLP/LLM Researcher to conduct advanced research and develop innovative applications focused on protecting and evaluating LLMs & agentic applications. In this role, you will collaborate with a multidisciplinary team of scientists and engineers, working together to address critical challenges. You will be responsible for designing, implementing, and evaluating new algorithms, models, and agents that help safeguard LLMs.

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‍Key Responsibilities:

Create real-time protections: Develop innovative methods to detect and mitigate adversarial attacks on agents and keep them in line with the organization's policy such as avoiding hallucinations, leaking personal data etc.
Develop new and unique evaluation methods: Implement and develop advanced metrics to evaluate agents' performance on multiple fronts.
Research and Development: Conduct research on adversarial attacks, model protections and performance evaluation techniques for NLP models. Explore and develop state-of-the-art methods and propose innovative solutions.
Collaboration: Work closely with other team members, participate in brainstorming sessions, and contribute to the team's success.

We're looking for an AI Engineer to design and own the agentic infrastructure that spans the entire company

What You'll Do

Architect and ship agentic capabilities as production-grade product features - owning the full tradeoff space across reliability, observability, security, latency, deterministic behavior and cost.

Design and build AI agents that operate across Oak's identity graph, automating complex identity related workflows.

Evaluate and integrate emerging LLMs, agent frameworks, and agent infrastructure, making principled build-vs-integrate decisions grounded in production requirements.

Partner with R&D and product leadership to identify where agentic automation delivers the highest leverage across the product.

As an AI Research Team Lead on Research group, you will mentor a talented team of researchers, set the strategic vision for research initiatives, and drive team-wide project goals. Youll partner closely with Product and Engineering leadership to turn open-ended data-security challenges into measurable experiments and shipped features. You will manage the critical balance between research exploration and product delivery, owning the end-to-end lifecycle-from problem framing and data strategy to evaluation, deployment, and ongoing monitoring-helping customers discover, protect, and govern their data at scale.



What Youll Do

Lead and mentor a team of AI researchers, fostering a culture of excellence and innovation while overseeing the end-to-end research lifecycle.
You will act as the technical and strategic lead, defining team priorities, roadmap, and data science methodologies.
Mentor and grow team members through technical guidance, career development, and peer reviews.
Collaborate with cross-functional leadership in Product and Engineering to align research efforts with core business objectives and customer needs.
Manage team performance, resource allocation, and timely project delivery within an agile environment.
Develop, evaluate, and maintain deep learning and NLP solutions to enhance Cyeras core capabilities in sensitive data classification.
Design and architect production-grade agentic workflows. Establish rigorous evaluation pipelines to benchmark agent accuracy, latency, and cost, ensuring reliable, scalable solutions for real-world customer problems
Innovation and creative thinking are the keys! Implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification.
Join a full stack AI group, including research engineering, MLEs, data operations, and security researchers. You will accelerate the path from research to production, ensuring results are both quick and precise.

As a Senior Data Scientist on Research team, youll develop and productionize ML/AI models that power our classifications and insights. Youll partner closely with Product and Engineering to turn open-ended data-security challenges into measurable experiments and shipped features. Youll own the end-to-end lifecycle-from problem framing and data strategy to evaluation, deployment, and ongoing monitoring-helping customers discover, protect, and govern their data at scale.



What Youll Do

Responsibility for an end-to-end research process. That includes identifying the problem, feature engineering, model development to deployment, outcome analysis, and refinement.
You will be a hands-on domain leader, laying the foundations of our data science workflows and algorithms. This is an excellent opportunity to work with endless amounts of user data and creatively generate insights that will increase the ability to classify tons of data.
Develop, evaluate, and maintain machine learning and NLP solutions to enhance core capabilities in sensitive data classification.
Innovation and creative thinking are the keys! Implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification.
Close interaction and collaboration with an excellent team of engineers, data analysts, and security researchers.

We are looking for a Data Science & ML-Ops Team Lead to lead a multidisciplinary team of Data Scientists and ML Engineers responsible for designing, building, deploying, and operating production-grade machine learning systems.
This is a highly technical leadership role that combines applied machine learning understanding, software engineering, distributed systems, and MLOps. You will own the end-to-end lifecycle of our AI capabilities - from data and feature engineering to model training, deployment, monitoring, experimentation, and continuous improvement.
You will play a key role in defining the architecture, engineering standards, and operational practices behind fraud detection systems that protect millions of users globally in real time.
If you are passionate about building intelligent systems at scale and transforming machine learning into reliable production services, we want to meet you.
What youll do:
Lead and mentor a team of Data Scientists and ML Engineers focused on fraud detection and response capabilities.
Build ML infrastructure focused on design, train, evaluate, and optimize machine learning models for real-time fraud prevention and risk assessment.
Own the lifecycle of ML models in production, including experimentation, deployment, monitoring, retraining, and performance optimization.
Drive customer-specific model training and tuning strategies to improve accuracy and adaptability across different customer environments.
Build and improve offline AI evaluation frameworks to measure model quality, drift, effectiveness, and business impact.
Collaborate closely with Engineering, Product, Security, and Data teams to deliver scalable and reliable AI-powered capabilities.
Define best practices for model serving, feature engineering, experimentation, observability, and operational excellence.
Balance model performance, latency, scalability, explainability, and operational constraints in high-scale production environments.
Promote a culture of technical excellence, continuous improvement, ownership, and innovation.

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.

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.

Required Algorithm Analyst & Tools Developer
Join us and help evaluate the performance of cutting-edge technologies powering the future of autonomous driving.
As an Algo Analyst & Tools Developer, you will validate the quality of complex algorithmic systems and build the infrastructure to analyze them.
You will examine large-scale data collected from drives all around the world, investigate system behaviors, and generate actionable insights.
Beyond traditional analysis, this role includes designing and developing AI-powered analysis tools that will help streamline and boost the productivity of our analysis and validation efforts.
What will your job look like:
Develop diagnostic tools for performance and quality metrics analysis.
Work to boost analysis efficiency by deploying AI-powered tools.
Develop and maintain automated analysis and monitoring workflows.
Present clear findings and recommendations through reports and summaries.
Collaborate with algorithm developers and product managers to validate new developments and drive future improvements by proactive problem finding.

We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.

Responsibilities:

Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.
Requirements