דרושים » תוכנה » Data Scientist

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.

about the job
the growth and notifications (gant) data science team's mission is to deliver the best of google's products to our users by enabling high-quality notifications and driving growth across the ecosystem. we are a team of data scientists and analysts who work to embed a deep empirical understanding of user behavior into the product development lifecycle. this enables our product partners to take smarter risks and build more engaging, high-quality experiences that drive sustainable growth.we are part of the broader ecosystem growth (eg) data science team within google identity and engagement (gie). our work is pivotal in increasing the number of users who experience the full value of google.this is an exciting opportunity to join a team that is at the forefront of driving user engagement and ecosystem growth at google.
responsibilities
lead large-scale data analysis and novel modeling approaches to create a deep understanding of user behavior with notifications and proactive agentic experiences.
partner with product, engineering, and UX research to define and develop novel evaluation metrics, validation methodologies, and experimentation frameworks for proactive quality models and ai-driven notification agents.
provide analytical thought leadership and advanced statistical expertise on user journeys, engagement models, and long-term causal effects across the gie and google ecosystem.
translate complex findings into actionable product recommendations, effectively presenting to cross-functional stakeholders at all levels to influence the product roadmap.
define, implement, and own key metrics to measure the quality, impact, and value of notifications and user-state models.

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 visionary and technically versatile Data Science Manager to join the Data Science & MLE group within our DS & Analytics organization.
In this pivotal role, you will lead a hybrid team of Data Scientists and Machine Learning Engineers, acting as the bridge between cutting-edge technical innovation and high-level business strategy. You won't just be managing a team; you will be the Technical Lead defining how we solve complex problems-from predicting player behavior to optimizing marketing budgets using the latest in Generative AI.
You will partner closely with Game Directors, Product Managers, and our central analytics teams to drive value across Zynga's diverse portfolio. If you are a leader who is "bilingual" in data-possessing deep Data Science expertise to guide methodology while bringing high MLE and Engineering skills to build scalable, production-ready systems-we want to hear from you.
Key Responsibilities:
Team Leadership & Mentorship: Recruit, retain, and develop top-tier talent within the Data Science & MLE team. Foster an inclusive culture of innovation where technical rigor meets creative problem-solving.

Technical Direction & Engineering Standards: Act as the hands-on Technical Lead for the domain. You will supervise the end-to-end development lifecycle-from research to production-enforcing high standards and MLOps to ensure our models are scalable and maintainable.
Strategic DS & Analytics Support: Drive the development of advanced analytical frameworks to solve core business challenges. You will guide the team in applying rigorous statistical and machine learning methods to areas such as Time Series Forecasting, Causal Inference, Marketing Optimization, Root Cause Analysis, and more.
Strategy & Collaboration: Partner with Game Directors, BI Platform PMs, and embedded analytics teams to define the data strategy and roadmap. You will ensure that our technical initiatives are directly aligned with company-wide goals and solving the right problems.
Global Alignment: Maintain a tight collaborative network with Data Science & Analytics managers in North America, ensuring global consistency in methodologies, knowledge sharing, and joint 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.

Were looking for an Applied Data Scientist to join one of our product squads. Youll design, build, and deploy data-driven solutions that combine machine learning, statistical methods, and SQL/rules-based decision logic to power autonomous supply chain intelligence platform. Youll work closely with data science, engineering, product, and supply chain experts and own solutions end-to-end-from problem definition to production monitoring and iteration.
Responsibilities:
Deliver data science solutions end-to-end within a product squad: problem framing → data prep/labeling → modeling → deployment support → monitoring → iteration
Build, train, and improve ML models for supply chain use cases (e.g., inventory risk prediction, demand anomalies, root-cause analysis)
Define success metrics and evaluation plans with support from senior DS/PM; run error analysis and document learnings
Work with stakeholders to create and maintain ground truth (label definitions, labeling workflows, QA checks, feedback loops)
Implement hybrid decision logic by combining ML outputs with statistical methods and SQL/rules-based logic for robustness and explainability
Analyze large, multi-source operational datasets to identify trends, anomalies, and drivers of performance
Collaborate with software engineers to productionize solutions (batch and/or real-time), including testing, logging, and basic monitoring
Monitor deployed models/rules, investigate performance issues (data quality, drift, edge cases), and iterate based on outcomes
Contribute to team practices: reproducible notebooks/code, documentation, and experiment tracking

Required Data Scientist, Product Analytics
As a Data Scientist, you will shape the future of people-facing and business-facing products we build across our entire family of applications. By applying your technical skills, analytical mindset, and product intuition to one of the richest data sets in the world, you will help define the experiences we build for billions of people and hundreds of millions of businesses around the world. You will collaborate on a wide array of product and business problems with a wide-range of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others. You will use data and analysis to identify and solve product development's biggest challenges. You will influence product strategy and investment decisions with data, be focused on impact, and collaborate with other teams. By joining us, you will become part of a world-class analytics community dedicated to skill development and career growth in analytics and beyond.
Product leadership: You will use data to shape product development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and us. You will help your partner teams prioritize what to build, set goals, and understand their product's ecosystem.
Analytics: You will guide teams using data and insights. You will focus on developing hypotheses and employ a varied toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches to test them.
Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
Data Scientist, Product Analytics Responsibilities
Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches
Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends
Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions
Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses
Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.

abra R&D is looking for a AI Engineer! abra R&D is looking for an AI Engineer that will take part of building a next-generation agentic analytics platform powered by a real-time, AI-optimized data infrastructure. We are looking for an experienced AI Engineer to design, build, and deploy intelligent systems that operate at scale and in real time. This role is hands-on and product-oriented, focusing on developing, integrating, and productionizing AI and machine learning models as part of a complex, high-performance platform. What You Will Do:
* Design, develop, and deploy AI and machine learning models into production systems
* Build scalable AI services that operate on large-scale and real-time data
* Implement deep learning and machine learning solutions using modern frameworks
* Integrate AI models into end-to-end product flows and backend systems
* Collaborate closely with software engineers and AI teams to deliver production-ready solutions
* Optimize model performance, reliability, and scalability in real-world environments
* Develop and maintain data pipelines and model-serving infrastructure
* Contribute to the evolution of AI-powered, agent-based systems and analytics capabilities

a palo alto networks company, is the global leader in identity security, trusted by organizations around the world to secure human and machine identities in the modern enterprise. ai-powered identity security platform applies intelligent privilege controls to every identity with continuous threat prevention, detection and response across the identity lifecycle. with identity security, organizations can reduce operational and security risks by enabling zero trust and least privilege with complete visibility, empowering all users and identities, including workforce, it, developers and machines, to securely access any resource, located anywhere, from everywhere.
job description:
as a data Scientist, youll join a team shaping the future of identity security and pioneering research in areas of cybersecurity and agentic ai.
advance your data science career by applying your established expertise to solve complex, real-world problems at scale and drive innovation alongside leaders in the cybersecurity and ai space.
research, design, evaluate and production deployment of advanced ai/ml models, specializing in llms, rag systems, and agentic ai applications.
explore and analyze large-scale datasets to uncover patterns and drive actionable insights.
collaborate closely with product managers, engineers, and analysts to support feature development and validate ml ideas.
stay up to date with the latest academic and industry trends in ai/ml and apply best practices to real-world challenges.
grow your skills in classic Machine Learning, deep learning, Natural Language Processing ( NLP ), llms and cybersecurity applications.

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.