דרושים » תוכנה » Data Scientist Expert

We are seeking an experienced and visionary Data Scientist to join our dynamic technology organization. The successful candidate will be a part of a team of talented data scientists, driving innovation and delivering business value through advanced machine learning techniques and Generative AI solutions. This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge data science methodologies, and a passion for continuous learning and development.
Responsibilities:
Gradient boosting techniques (e.g., XGBoost, LightGBM, CatBoost) are used to enhance predictive accuracy and model robustness.
Drive the exploration and integration of Generative AI applications, including Large Language Models (LLMs), to create innovative solutions for our products and services.
Collaborate with cross-functional teams (engineering, product, business) to translate business requirements into actionable data science projects.
Establish best practices for model development, validation, deployment, and monitoring in production environments.
Promote a data-driven culture, encouraging experimentation, sharing of knowledge, and adoption of state-of-the-art technologies.
Communicate project progress, insights, and results to stakeholders at all levels of the organization. Design, implement, and optimize classic machine learning models to solve complex business problems.

We are looking for a highly technical and innovative, hands-on Data Scientist to join our Threat Research group.

Our Threat Research group is composed of elite researchers and developers. We research applications, DDoS, and database attacks, develop algorithms for new products, and drive innovation and thought leadership in cybersecurity.

Key Responsibilities:

Lead and contribute to the development, evaluation, and deployment of machine learning, deep learning, and large language models (LLMs) for cybersecurity applications.

Drive and support data-driven research projects, from ideation and prototyping to production deployment, using both classic ML and advanced deep learning techniques.

Perform comprehensive data collection, preprocessing, analysis, and feature engineering on large-scale security datasets.

Develop and optimize innovative security solutions for integration into Imperva products.

Collaborate closely with data scientists, security researchers, engineers, and product teams to deliver impactful solutions.

Actively participate in knowledge sharing, code reviews, and continuous team learning.

Required Data Scientist
About the Role
The Data Science department plays a pivotal role in our company, generating value to us by developing algorithms and analytical production-grade solutions. We leverage advanced techniques and algorithms to provide maximum value from data in all shapes and sizes (such as classification models, NLP, anomaly detection, graph theory, deep learning, and more). As a Data Scientist, you will assume the classic data-science role of an end-to-end project development and implementation practitioner. Being part of the team requires a mix of hard quantitative and analytical skills, solid background in statistical modeling and machine learning, a technical data-savvy nature, along with a passion for problem-solving and a desire to drive data-driven decision-making.
What You'll Be Doing
Data Exploration and Preprocessing: Collect, clean, and transform large, complex data sets from various sources to ensure data quality and integrity for analysis
Statistical Analysis and Modeling: Apply statistical methods and mathematical models to identify patterns, trends, and relationships in data sets, and develop predictive models
Machine Learning: Develop and implement machine learning algorithms, such as classification, regression, clustering, and deep learning, to solve business problems and improve processes
Feature Engineering: Extract relevant features from structured and unstructured data sources, and design and engineer new features to enhance model performance
Model Development and Evaluation: Build, train, and optimize machine learning models using state-of-the-art techniques, and evaluate model performance using appropriate metrics
Data Visualization: Present complex analysis results in a clear and concise manner using data visualization techniques, and communicate insights to stakeholders effectively
Collaborative Problem-Solving: Collaborate with cross-functional teams, including product managers, data engineers, software developers, and business stakeholders to identify data-driven solutions and implement them in production environments
Research and Innovation: Stay up to date with the latest advancements in data science, machine learning, and related fields, and proactively explore new approaches to enhance the company's analytical capabilities.

We are looking for an experienced Data Scientist who can leverage and develop innovative machine learning, NLP, and generative AI technologies, for solving problems within a wide scope.
As a part of our Data Science group, you will work on highly complicated challenges, from research to production, and help shape our algorithms and AI platform. You will work with Developers, Analysts, Product Managers, and business owners on growth opportunities and new ideas.
You should have a strong background and experience in the machine learning pipeline through business understanding, research, data exploration, feature engineering, model building, performance evaluation, testing, production-level code and deployment, model monitoring, and maintenance.
What am I going to do?
Take leading roles in projects such as search engine ranking, promoted ads in our companys search results, recommendations, users segmentation and personalization, online bidding optimization, text analysis, real-time predictions, live streaming data handling, exploration-exploitation problems, etc.
Work closely with various departments in the company to provide advanced Data Science solutions end to end.
Write and own production models and code.
Independent research and innovation in new content and technological domains.
Lead and advise other team members.
Continuously produce new initiatives and improvement ideas.
Act as a leader of our companys AI platform. Develop models and framework solutions for the group.

Our algorithms group is responsible for all of the AI and algorithmic challenges the company is facing, tackling complex real-world problems that directly affect the companys success. As a lead data-scientist, you will lead a new research area and a small team within the company. This is a primarily hands-on role where you will build the AI workflows that digest a large amount of construction documents and architectural models to help with one of the product core processes.
What Youll Do:
Lead and mentor a small team while remaining deeply hands-on in all development aspects-from design to production-of LLM-based workflows to digest information from a variety of construction-oriented documents
Research, design, and integrate LLMs to deliver advanced automation and reasoning capabilities
Develop datasets and benchmarks to evaluate and monitor LLM-based applications effectively
Build LLM evaluation and optimization processes.

We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.

As a Research Engineer , you will design, develop, and implement algorithms and models that will enhance the products capabilities, performance, reliability, and scalability. Together with our algo and engineering team members, you will work closely with our product team members to implement algorithms that can be used to drive our business forward as well as the frontier of software development AI technologies.

Responsibilities:

Design, develop, implement and test algorithms and AI-empowered solutions for various product features and applications
Collaborate with cross-functional teams to understand business needs and translate them into algorithmic solutions
Perform research on emerging trends, algorithms, and cutting-edge technologies, and identify ways to incorporate them into our products pragmatically
Propose new and innovative solutions to complex problems and lead the development of algorithms in those areas
Design, cleanse, and utilize benchmarks throughout the process of the algorithm development

We are looking for a Data Scientist to join our team.
The ideal candidate is a highly motivated and self-driven individual with strong analytical and problem-solving skills. The Data Scientist will be responsible for analyzing data and developing models to help inform decisions and strategies for new products and business initiatives.
Responsibilities:
Work closely with stakeholders to understand business requirements and develop predictive models to detect fraud.
Develop and implement statistical models, machine learning and deep learning algorithms to solve complex problems in a production environment.
Perform exploratory data analysis and data visualization to gain insights and communicate findings.
Collaborate with cross-functional teams, including data engineers and software developers, to design and implement end-to-end solutions that can be deployed in production.
Implement advanced analytics tools and technologies to enhance reporting capabilities.

We are looking for a Data Analyst with strong Python and engineering capabilities to join our Data team.

This is not a traditional analytics role - it combines data analysis with hands-on development. You will work extensively with Python as part of your day-to-day work, building scalable data processes, evaluation frameworks, and classification logic that directly impacts our product.

Our team works on large-scale data systems, leveraging ML capabilities such as NER and LLMs, alongside robust engineering and analytics to deliver insights across millions of files and tables daily.


Responsibilities
Design and implement data classification logic to expand coverage of sensitive data types, owning the process end-to-end - from research and analysis to production deployment
Develop Python-based workflows and tools for data processing, evaluation, and automation
Build and maintain data pipelines and monitoring systems to track product performance and data quality
Create automated frameworks to evaluate and benchmark model performance
Collaborate closely with R&D and take part in discussions around AI/ML solutions and product direction

This is a research‑first role focused on deeply understanding LLM internals to improve the security of AI agents. Youll design careful experiments on activations and interpretable features- e.g., probing, attribution & ablation/patching, representation‑geometry analyses-to uncover mechanisms behind jailbreak, indirect prompt injection, and other attacks. Then translate those insights into signals that can be used for detection and analysis of a model response.
The field of LLM interpretability at scale is exploding, with several major publications in the last months, and major opportunities for innovation.
What Youll Do:
Investigate model internals, including activation/features analysis, unsupervised clustering, discovery of directions in latent space, etc. It may also require training specific model parts to improve interpretability metrics.
Design security‑grounded evaluations: curate datasets for different attack types, evaluate performance of different white box (model internals) methods compared to black box (input/output only) baselines.
Publish and share: produce Labs posts and open artifacts; when the work is strong, aim for tier‑1 ML venues (NeurIPS, ICML, etc.) and security forums. A publication of code and/or trained models in cases of community relevant novelty.
Build tools: Several open source libraries exist (like Anthropics attribution graphs infra), but the research in the field is very dynamic, which will require you to build and adapt tools to your own research directions. This also includes agents to automate research work and distill knowledge from designed experiments.