דרושים » דאטה » Senior data Scientist

Were looking for a Senior MLOps Engineer 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.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do

Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.

Req ID: 29223

As a Machine Learning Scientist, you will design, build, and deploy advanced models that guide pricing and promotional optimization across our company. You will work closely with other scientists, engineers, analysts, and product teams to translate complex business challenges into scalable, data-driven solutions that deliver measurable impact.

Key Job Responsibilities and Duties:

Develop and deploy models for causal inference, uplift estimation, and optimization to measure and maximize the incremental effect of price and promotion decisions.

Design and improve dynamic pricing algorithms that balance competitiveness, conversion, and profitability.

Contribute to the development of platform capabilities, enhancing experimentation, simulation, and decision-support capabilities.

Partner with product and business stakeholders to translate scientific insights into actionable strategies.

Stay up to date with the latest advances in machine learning, causal modeling, and pricing optimization, and apply them pragmatically at scale.

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 are seeking a Machine Learning Engineer to join the team that builds the predictive intelligence powering the Hello Heart app. You will own the ML models behind user engagement, cardiovascular risk stratification, and personalized health recommendations - the systems that determine what users see, when they're nudged, and how their health trajectories are shaped.

This role demands both statistical depth and engineering proficiency - you will be expected to take models from research through to deployment, write code built for production, and use AI coding assistants fluently as part of how you get work done.

Responsibilities
Lead end-to-end development of predictive ML models. From data exploration and feature engineering through training, validation, deployment, and ongoing monitoring across engagement and clinical risk domains.
Apply strong statistical foundations to model design, feature selection, uncertainty quantification, and interpretation of results
Write production-grade Python code that is clean, tested, and built for maintainability and scale.
Use AI coding assistants to accelerate development, code review, and documentation without sacrificing quality or rigor.
Partner with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
Research and implement cutting-edge ML techniques spanning supervised and unsupervised learning, causal inference, deep learning, and reinforcement learning to tackle complex healthcare challenges.
Contribute to MLOps infrastructure: model serving, versioning, evaluation pipelines, and monitoring.
Design and interpret A/B tests and other experimental methodologies to measure the impact of models, features, and interventions.

We are looking for a Senior Data Scientist to join AI team, the team behind underwriting decisions, quote-time risk scoring, and portfolio analytics.
In this role, you will own entire research directions: framing the problem, deciding what's worth measuring, designing the approach from first principles, and turning
the result into production signal. The work is production-oriented applied research on hard, real-world datasets where standard recipes don't apply and the right method
often has to be invented for the problem.
What You'll Do:
- Own open-ended research directions end-to-end - from a vague business question to a deployed, validated signal.
- Work across the team's core research areas - risk modeling, environmental and catastrophe modeling, and behavioral modeling - translating complex real-world processes
into reliable predictive signal.
- Deeply investigate existing production models - understand what they actually learn, where they break, and where the headroom is; identify and ship optimizations
grounded in that understanding.
- Design approaches from first principles when off-the-shelf methods don't fit the data.
- Build rigorous evaluations and own the result in production, not just the notebook.
- Document research clearly so findings are reproducible, auditable, and compound over time.

Are you passionate about harnessing cutting-edge data science techniques to protect the world from cyber threats? Do you thrive in the dynamic world of cybersecurity? If so, we invite you to join our innovative and forward-thinking team at our company, where you'll have the opportunity to shape the future of cybersecurity and impact millions of customers.
As a Senior Principal Data Scientist, you will be a technical leader and hands-on architect, bridging the worlds of advanced data science, robust software engineering, and next-generation AI. You will bring a deep, diverse background in classical machine learning algorithms alongside cutting-edge LLM agent architectures to build resilient, production-grade defense techniques against cyber-villains. Operating as a premier technical authority, you will drive the vision, design, and large-scale development of our groundbreaking, Agentix LLM-based security solutions that block attacks even before they begin.
Key Responsibilities
Serve as a hands-on technical compass for a diverse, elite product-oriented research team, pioneering state-of-the-art technologies and setting the engineering standards for AI/ML excellence across the organization.
Design, implement, and optimize robust Agentic LLM solutions and multi-agent workflows engineered to autonomously counter modern, sophisticated cyber threats.
Oversee and actively contribute to the entire end-to-end lifecycle, moving seamlessly from ambiguous research concepts and POCs to high-throughput, low-latency production deployments.
Build and enforce comprehensive frameworks for continuous real-time monitoring, evaluation, and guardrails to track model drift, latency, and agent behavior in production environments.
Decompose highly complex, multi-layered algorithmic problems into actionable technical roadmaps and strategic insights for both deeply technical engineers and executive business stakeholders.

Are you passionate about harnessing cutting-edge data science techniques to protect the world from cyber threats? Do you thrive in the dynamic world of cybersecurity? If so, we invite you to join our innovative and forward-thinking team at Palo Alto Networks, where you'll have the opportunity to shape the future of cybersecurity and impact millions of customers.
As a Senior Principal Data Scientist, you will be a technical leader and hands-on architect, bridging the worlds of advanced data science, robust software engineering, and next-generation AI. You will bring a deep, diverse background in classical machine learning algorithms alongside cutting-edge LLM agent architectures to build resilient, production-grade defense techniques against cyber-villains. Operating as a premier technical authority, you will drive the vision, design, and large-scale development of our groundbreaking, Agentix LLM-based security solutions that block attacks even before they begin.
Key Responsibilities
Serve as a hands-on technical compass for a diverse, elite product-oriented research team, pioneering state-of-the-art technologies and setting the engineering standards for AI/ML excellence across the organization.
Design, implement, and optimize robust Agentic LLM solutions and multi-agent workflows engineered to autonomously counter modern, sophisticated cyber threats.
Oversee and actively contribute to the entire end-to-end lifecycle, moving seamlessly from ambiguous research concepts and POCs to high-throughput, low-latency production deployments.
Build and enforce comprehensive frameworks for continuous real-time monitoring, evaluation, and guardrails to track model drift, latency, and agent behavior in production environments.
Decompose highly complex, multi-layered algorithmic problems into actionable technical roadmaps and strategic insights for both deeply technical engineers and executive business stakeholders.

We are seeking a detail-oriented and collaborative Senior ML Engineer to help support and maintain our machine learning capabilities. This role is ideal for someone who enjoys working closely with production systems, ensuring reliability, scalability, and explainability of models while enabling research teams to deliver impact faster.



Responsibilities



Collaborate with cross-functional teams to ensure ML systems remain robust, explainable, and aligned with business needs.
Monitor and report on ML model performance, reliability, and explainability metrics.
Participate in model retraining procedures, implement automation and optimization of MLOps pipelines.
Extend and scale monitoring pipelines, including support for new features in development.
Investigate, troubleshoot, and resolve issues in production ML workflows (tiered support from initial triage to root-cause analysis with model owners).
Develop and maintain repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools.
Perform exploratory data analysis (EDA) on historical datasets to identify quality issues and maintain data health.
Implement and oversee production based adjusters across customer deployments.
Evaluate and track critical ML artifacts such as explainability files, coverage metrics, and alignment of features.
Support development and maintenance of internal tools (e.g., interfaces, registries, and feature monitoring frameworks).
Build and maintain static and temporal features, including seasonality, event-based, and price-related features.

We are looking for an experienced Data Product Manager with a strong Data Science and Analytics background to own and lead data driven solutions.
In this role, you will define and drive the vision, strategy, and execution of data and analytics products that support our Marketing, Growth, and Acquisition teams. You will operate at the intersection of business, analytics, data science, and AI, translating marketing challenges and business questions into scalable data products, clear KPIs, and measurement frameworks that improve acquisition efficiency, ROI, and long-term revenue growth.
You will lead cross-departmental initiatives, partner closely with Marketing leaders, Data Scientists, Data Engineers, Analytics teams, and Product Managers, and ensure that advanced modeling, analytics, and AI capabilities are deeply embedded into day-to-day marketing decision-making.
Key Responsibilities
Marketing Data Product Ownership
Own the vision, roadmap, and delivery of Marketing Data Science and Analytics products
Align initiatives with business goals such as acquisition efficiency, ROI optimization, revenue growth, and funnel performance.
Act as the single point of ownership for marketing-related data, analytics, and data science products across teams.
Define data requirements, metrics, and success criteria for new initiatives, including identifying gaps in existing data.
Collaborate with Data Engineers and Analytics Engineers to design scalable data pipelines, data models, and semantic layers that support analytics, experimentation, and advanced modeling.
Prioritize initiatives based on business impact, scalability, data maturity, and clarity of success metrics.
Business Analytics, Data Science & AI-Driven Marketing Optimization
Define and own the business logic behind marketing KPIs, ensuring clear, consistent, and trusted metrics across teams.
Work closely with stakeholders to understand what needs to be measured, why and how.
Lead data science, analytics, and AI initiatives that optimize marketing performance
Own and evolve core marketing models including attribution, MMM, LTV prediction.
Define success metrics and measurement frameworks for models, experiments, and AI-driven decisions.
Drive experimentation frameworks (A/B tests, geo-experiments, uplift modeling) to continuously improve performance and decision quality.
Design and deploy AI-powered and agentic solutions to automate and scale marketing workflows such as budget allocation, campaign optimization, and creative testing.
Ensure solutions are production-ready, measurable, explainable, and actionable for business stakeholders.
Translate complex model outputs into clear, trusted insights for business users.

We are looking for a Senior Data Scientist to lead the design and implementation of next-generation AI solutions for Lifecycle Marketing.
As a senior member of our team, you will shape methodology, guide product direction under uncertainty, and represent in high-stakes conversations with customers. Youll combine deep expertise in causal inference, reinforcement learning, and experimentation with strong communication skills to drive impact across the company and with our clients.
Responsibilities:
Own the design and methodology of data science solutions for lifecycle marketing, with emphasis on causal inference, uplift modeling, and reinforcement learning.
Lead the development of experimentation frameworks (A/B/n tests, variance reduction, policy evaluation).
Research, prototype, and productionize multi-armed bandits and contextual RL approaches for personalization and timing optimization.
Take a strong, opinionated role in brainstorming sessions, guiding decision-making in areas of high uncertainty.
Represent Voyantis in front of customers, translating complex modeling decisions into clear business value.
Mentor junior and mid-level team members, raising the technical bar across the team.
Collaborate closely with product managers, engineers, and analysts to align data science strategy with business goals.