לוח משרות דרושים Data Scientist

As a Data Scientist , you will be a part of a large and innovative data science group, working closely with Dev teams and product managers, to create business value through Data Science and Machine Learning methods using cutting edge technology, on large and diverse datasets.

The Role consists of understanding customers' needs and product requirements, exploring data, identifying and defining functionality from conception to delivery, creating and improving algorithms and ML pipelines, building measurement tools, providing data visualizations, and more.

Deliver end-to-end ML products - including data processing, problem analysis, research, model development, validation and metrics, implemented in production and with online testing.

Be a core member of a cross-functional AI innovation team, collaborating closely with software engineers, MLOps specialists, and analysts.
Design, build, and optimize end-to-end AI-driven features - from experimentation and model training to validation and deployment.
Develop production-grade code (Python and C#) to integrate advanced models and data-driven capabilities into our core products.
Continuously evaluate model performance and data quality in real-world settings, proactively driving improvements, retraining, and robust monitoring.

We are seeking an Analytics Functionality Analyst to join our Analytics engineering group. In this role, you will serve as a crucial partner to the Product Manager, facilitating the translation of complex analytics into actionable steps for functional software delivery. This hybrid position combines robust analytical skills with a keen understanding of engineering processes, allowing the Product Manager to focus on the strategic “what” and “why,” while you ensure clarity on the operational “how.” What you’ll do: Feature Translation: Convert high-level analytical concepts and product features into clear, functionally defined user stories that capture algorithmic and statistical nuances for the engineering team. Collaboration with Engineering Teams: Work closely with developers and data scientists to assess feature feasibility, identify edge cases, and establish thorough testing and validation criteria within the engineering environment. Customer Engagement: Participate in technical discussions with customers, especially when deep modeling insights or analytics expertise are required, to gather feedback that informs engineering decisions. Documentation Authority: Own the documentation of analytics functionality, positioning yourself as the internal authority on modeling logic and assumptions, thereby ensuring consistency and clarity across engineering projects. Support Engineering Testing: Drive the testing and validation of implemented models and features, ensuring they function as expected from an analytics perspective and contributing to overall product quality.

Position Intro:
Earnix is the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. Earnix has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.

As a Data Scientist, you will sit at the very core of our company. This isn't just a role about maintaining models-its about transformation. You will be the architect of a "learning machine," turning raw data into usable metrics and developing the statistical algorithms that power our business performance.

You will collaborate with Data Engineers and Product Managers to translate the "voice of the customer" into digital insights that define the industry standard.

Key Responsibilities:
Drive Actionable Insights: Analyze diverse data sources to create insights that directly fuel business growth.
Optimize Performance: Build advanced ML algorithms for bidding-utilizing classification and clustering to maximize campaign performance.
Innovate with GenAI: Leverage Generative AI techniques to query data for intelligent insights and automate decision-making.
Own the Lifecycle: Lead projects from initial exploration and Proof of Concept (POC) to scalable production environments.
Scalability & Performance: Ensure our data pipelines and ML models can handle large-scale, dynamic datasets with high efficiency.
Metric Ownership: Create and own key performance metrics that drive innovative 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 our business. Together with other Data Engineers and Product Managers, you will translate the voice of our customer into best-in-class digital insights.

Responsibilities:
Analyzing data from various sources and transforming it into actionable insights that directly drive business growth.
Building machine learning algorithms for bidding - using classification and clustering to optimize campaign performance.
Leveraging artificial intelligence (GenAI) techniques to query data for intelligent insights and automated decision-making processes, unlocking hidden value from data.
Leading tasks from exploration to proof of concept (POC) and ensuring smooth transitions to scalable production environments.
Collaborating with data engineers and product managers to ensure the delivery of high-quality production results.
Creating and owning key metrics while developing new models and capabilities for innovative product solutions.
Optimizing scalability and ensuring the performance of data pipelines and ML models, handling large-scale, dynamic datasets.

Were looking for a Data Scientist to join our R&D department.
Why is this role so important?
we are 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.

Responsibilities:
Design, implement, and improve ML and AI models to drive business outcomes across multiple domains, such as recommendation systems, image recognition, ChatBot, etc.
Own the end-to-end ML lifecycle: data preprocessing, feature engineering, model training, validation, and deployment.
Expert level in designing evaluation pipelines to prove performance, scalability, and consistency
Leverage generative AI and LLMs to enhance existing workflows and explore new product opportunities.
Collaborate with Product, Engineering, and Analytics teams to align modeling efforts with business needs.
Clearly communicate complex findings and model insights to stakeholders.

We're looking for a seasoned Senior Data Scientist to be a hands-on technical powerhouse and strategic driver on our team. You wont just build models; you will personally architect and implement the next generation of our decision engines, owning our most complex modeling challenges from whiteboard to production.

As a senior, hands-on member of the team, you will be a go-to expert. While you'll guide and mentor other data scientists, your primary focus will be on leading by example: executing on high-impact projects that define our competitive edge and directly influence our revenue. You'll contribute to the roadmap, not just follow it.


What Youll Do

Architect & Build: Lead the architectural design and hands-on implementation of our most critical ML models, tackling complex challenges in auction dynamics, user valuation, and more at a massive scale.

Influence Strategy: Contribute to and influence the data science roadmap in collaboration with Product and Engineering, ensuring our efforts are focused on maximum business impact.

Outsmart the Competition: Lead the charge in our high-stakes auction environment where your models edge creates outsized financial returns. You will design and own the experimentation framework to prove your impact.

Elevate Our Technical Bar: Set best practices for modeling and coding, and mentor other data scientists through complex technical challenges, code reviews, and model design sessions.

We are looking for a talented Data Scientist who is a strong team player to join our Data Science team. As a Data Scientist, you will play a key role in shaping our analytics pipeline. Dive into diverse datasets, using ML algorithms, and lead end-to-end projects - starting from gathering requirements, through development, to deployment and presentation to stakeholders. Your Python expertise, along with data-driven thinking, will contribute to predictive insights and innovative solutions.
Key Responsibilities
Own end-to-end development cycle- from business understanding to development, deployment and monitoring
Develop, maintain and improve advanced AI/ML models with a wide range of tasks and diverse datasets (structured, text, images)
Interpret data using statistical techniques and leverage machine learning algorithms to drive actionable insights.
Collaborate with cross-functional teams, including engineers and product managers, to understand business challenges and design data-driven solutions.

Were looking for a Data Scientist to turn complex data into actionable insights that drive product, operations, and business decisions.
You will own the full analytics and modeling lifecycle-from problem framing and data wrangling to experimentation, deployment, optimization and communicating your output to the customer.
Responsibilities
Partner with product, engineering, and business teams to define problems and success metrics
Explore, clean, and transform large, messy datasets from multiple sources
Build and validate statistical and machine learning models (e.g., classification, regression, time series, NLP, recommendation, anomaly detection)
Translate findings into clear narratives, dashboards, and recommendations that influence decisions
Productize models and monitor performance
Establish data quality checks and model governance (bias and drift monitoring)
Document methods, assumptions, and results; present to technical and non-technical audiences.

We are looking for an experienced ML/AI engineer to anchor our ML Platform team. You will lead the effort to productize our Large Language Models, transforming experimental code into robust, efficient services. Your goal is to build the infrastructure that allows our Data Scientists to move fast without breaking production.

What you'll be doing:
Model Development & Optimization

Fine-tuning & Engineering: Develop and fine-tune LLMs/classic models for specific medical tasks (condition detection, dialogue systems) using internal datasets.
Research to Production: Partner with Data Scientists to develop experimental code into scalable, production-ready modules.
Platform Engineering & Infrastructure

Pipeline Orchestration: Build and maintain complex ML workflows using Kubeflow Pipelines (KFP) or similar orchestration tools.
Internal Tooling: Develop and manage the internal Python ecosystem-libraries, SDKs, and utilities-that the Data Science team uses for daily development.
CI/CD & Automation: Write and maintain CI/CD scripts to automate the testing, versioning, and deployment of machine learning artifacts.
Production Standards & Integration

System Integration: Work with backend developers to integrate trained models/agents into the core application architecture.
Code Quality: Enforce high engineering standards through code reviews, ensuring that research code meets production reliability and maintainability requirements.
Requirements Engineering: Translate evolving data science requirements into concrete infrastructure and platform features.

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.

we are seeking a talented data Scientist to join our team. In this role, you will develop and implement ML models to support risk assessment and decision-making processes. The ideal candidate combines strong analytical skills with practical experience in building and deploying Machine Learning models in a financial or fintech environment. Responsibilities:
* Develop, validate, and implement Machine Learning models (Regression, Classification, Supervised/Unsupervised).
* Analyze large datasets to extract insights and support decision-making.
* Collaborate with cross-functional teams to integrate models into production systems.
* Apply Gen-AI techniques where applicable to enhance model performance.
* Document model development processes and results.