We are The Data Science team, focused on Risk Management, our work is centered around compliance and due diligence, specifically Fair Lending and Anti Money Laundering by unmasking the bad guys and solving problems. Our team is working with vital analytics to ensure we are within the guidelines for banking regulations, managing the bank's exposure to loss and risk. Our team uses SAS, Python and various machine learning models to provide on-demand analysis. You will Manage a team that will design, develop, and implement predictive models, machine learning algorithms, and statistical techniques to assess fair lending risks and detect potential instances of discrimination Analyze large datasets and identify patterns, trends, and potential areas of concern related to Fair Lending practices Utilize advanced statistical methods to evaluate model performance, including model calibration, validation, and interpretation of results Collaborate with cross-functional teams to understand business requirements and develop data-driven solutions for Fair Lending compliance Develop and maintain of scalable data pipelines, data models, and analytical tools to support fair lending analysis and reporting activities Be committed to diversity, equity, and inclusion, with a passion for promoting fairness and equality in lending practices This position reports to the Fair Lending Data Science Manager
You have
* 5+ years of experience in data analysis, statistical modeling, and predictive analytics within the financial services industry, preferably in Fair Lending or credit decisioning - Must
* Bachelor's degree in Statistics, Mathematics, Data Science, Economics, or a quantitative field. - Must ( Advance degree an advantage) Proficient in Hebrew and English both written and verbal, sufficient for achieving consensus and success in a remote and largely asynchronous work environment - Must
* Strong knowledge of advanced analytics and machine learning techniques such as regression and classification algorithms including linear and logistic regression, k-nearest neighbors, support vector machines (SVMs) and other techniques
* Experience working with cloud-based data platforms and technologies such as AWS and Sagemaker for scalable data analytics and machine learning
* Strong technical proficiency in data science tools and programming languages such as Python, R, SQL , with experience in developing predictive models and machine learning algorithms
* Experience working with large-scale datasets
* Ability to communicate findings and recommendations to stakeholders
* Strong problem-solving skills while paying attention to detail
* Strong project management skills, with the ability to manage multiple projects, prioritize tasks, and meet deadlines in a fast-paced, dynamic environment
* Ability to facilitate collaboration to achieve project goals A Bonus
* 2+ years of experience in a leadership or management role an advantage
* Familiarity with Fair Lending regulations such as the Equal Credit Opportunity Act (ECOA), Fair Housing Act (FHA), and Home Mortgage Disclosure Act (HMDA) preferred
* Experience in Fair Lending analytics preferred
* Knowledge of regulatory compliance frameworks and industry guidelines for fair lending risk management an advantage
* Experience with machine learning techniques, such as decision trees, random forests, or gradient boosting an advantage
* Experience with data visualization tools such as Tableau, Power BI an advantage Why join us Cross River Technologies develops software infrastructure for the fintech industry. Providing innovative technological solutions for various financial services, including payments, credit cards, and loans. Globally, Cross River has 900 employees, 170 of whom are based in the R&D center at the Jerusalem offices. We offer a wide range of wellness benefits including ong

We are The data Science team, focused on Risk Management, our work is centered around compliance and due diligence, specifically Fair Lending and Anti Money Laundering by unmasking the bad guys and solving problems. Our team is working with vital analytics to ensure we are within the guidelines for banking regulations, managing the bank's exposure to loss and risk. Our team uses SAS, Python and various Machine Learning models to provide on-demand analysis. You will Design, develop, and implement predictive models, Machine Learning algorithms, and statistical techniques to assess fair lending risks and detect potential instances of discrimination Analyze large datasets to identify patterns, trends, and potential areas of concern related to Fair Lending practices Utilize advanced statistical methods to evaluate model performance, including model calibration, validation, and interpretation of results Collaborate with cross-functional teams to understand business requirements and develop data -driven solutions for Fair Lending compliance Be committed to diversity, equity, and inclusion, with a passion for promoting fairness and equality in lending practices You have Proficient in Hebrew and English both written and verbal, sufficient for achieving consensus and success in a remote and largely asynchronous work environment - Must
Why join us
Our company develops software infrastructure for the fintech industry. Providing innovative technological solutions for various financial services, including payments, credit cards, and loans. Globally, the company has 900 employees, 170 of whom are based in the R&D center at the Jerusalem offices. We offer a wide range of wellness benefits including ongoing learning and development opportunities; monthly wellness reimbursement; volunteering opportunities; charitable donation matching; Keren Hishtalmut on full salary; private health and dental insurance; advanced study fund and various company events.

Required Data Scientist
DESCRIPTION
We are:
The Data Science team, focused on Risk Management, our work is centered around compliance and due diligence, specifically Fair Lending and Anti Money Laundering by unmasking the bad guys and solving problems. Our team is working with vital analytics to ensure we are within the guidelines for banking regulations, managing the bank's exposure to loss and risk. Our team uses SAS, Python and various machine learning models to provide on-demand analysis.
You will:
Design, develop, and implement predictive models, machine learning algorithms, and statistical techniques to assess fair lending risks and detect potential instances of discrimination
Analyze large datasets to identify patterns, trends, and potential areas of concern related to Fair Lending practices
Utilize advanced statistical methods to evaluate model performance, including model calibration, validation, and interpretation of results
Collaborate with cross-functional teams to understand business requirements and develop data-driven solutions for Fair Lending compliance
Be committed to diversity, equity, and inclusion, with a passion for promoting fairness and equality in lending practices.

We're looking for highly talented, experienced, passionate, and data-driven Senior Data Scientists.
Create and develop ML models to drive the company forward and support decision making; while using data driven insights about our product, customers, business goals
Work in a team and independently, understand business requirements and how they relate to and support business KPIs.
Research creative data-driven ways to solve challenging real-world business problems
Design, establish, maintain and evolve the companys machine learning (ML) models
Develop production-level, scalable ML pipelines and models
Collaborate with analytics, business and data engineering teams and bring a proactive approach to handling anomalies and abnormal behavior within our DWH
Use your background in predictive modeling, advanced statistical analysis, ML practices and techniques and time series modeling to build new features and products.

We're looking for experienced, creative and highly independent NLP algorithm engineers to join our winning team.
This is a great opportunity to join an algorithm department early on and to have a major impact on the product and methods used.
You will be in charge of end to end algorithm development, from initial research, all the way to full deployment.
RESPONSIBILITIES:
Research, develop, and deploy advanced machine learning and deep learning models to production.
Take end-to-end ownership of the project lifecycle, from data collection and preprocessing to model training, evaluation, and deployment.
Utilize NLP methodologies to extract and provide insights from patient medical records.
Collaborate closely with cross-functional teams to understand business requirements and translate them into data-driven solutions.
Stay updated with the latest research and advancements in machine learning, deep learning, and NLP.
Communicate findings and recommendations effectively to both technical and non-technical stakeholders.

We are seeking a highly skilled and motivated Cyber Data Analyst to join our dynamic team.
The ideal candidate will be proficient in working with both structured and unstructured data, utilizing a variety of databases including GraphDB, VectorDB, ColumnDB, Elasticsearch, and other NoSQL databases.
This role involves leveraging Python to explore data, extract insights, present trends, and make predictions.
Responsibilities:
Advanced Data Exploration: Apply Python and statistical tools to deeply explore structured and unstructured data across various database technologies (GraphDB, VectorDB, ColumnDB, Elasticsearch, and NoSQL).
Insight and Trend Analysis: Extract meaningful insights from complex data sets to identify trends, patterns, and anomalies.
In-depth Research: Research up-to-date threat-intelligence repositories and automate data enrichments into the platform.
Data-Driven Cybersecurity Analysis: Utilize advanced statistical methods and mathematical modeling to analyze data for potential threats and vulnerabilities, focusing on patterns and anomalies that could indicate security incidents.
Collaborative Insight Sharing: Work alongside cross-functional teams to communicate complex data findings. Prepare and present reports that translate intricate data insights into actionable intelligence for both technical and non-technical stakeholders.
Continuous Skill Enhancement: Stay updated with the latest statistical methods, data analysis techniques, and advancements in AI, including machine learning and deep learning, to continually improve cybersecurity data analysis practices.

Our R&D and Innovation Center is looking for a Data Scientist
We are seeking a seasoned Data Scientist with a strong background in optimization and Bayesian methods. The ideal candidate will have a deep understanding of data science principles, machine learning, and statistics concepts.
The Product:
ConvergeCONSUMER is a solution designed to help businesses understand and predict consumer behavior. It combines customer data, next-generation technology platforms, AI, and machine learning to predict consumer demand.
The solution leverages decades of experience in the consumer sector to drive value for clients. It helps businesses understand their customers' spending behaviors and preferences by combining CRM data with external data.
In this role you will:
Apply data science techniques and build high quality predictive models.
Leverage optimization/Bayesian methodologies to solve complex business problems.
Utilize programming and database languages.
Address real-world analytical problems using machine learning, statistical modeling, or similar quantitative approaches.
Develop and deploy models in cloud-based ecosystems such as GCP, AWS, or Azure.
Collaborate with cross-functional teams to understand business requirements and provide data-driven solutions.
Full time Job
Location: Tel Aviv, Hybrid.

We are looking for a highly motivated and experienced Principal Data Scientist to join our team! The Education Insights team in our Education builds and operates experiences to empower and delight learners and educators for the betterment of education of millions of users worldwide.

The successful candidate will have strong engineering capabilities with high Python proficiency and a deep understanding and experience in machine learning and AI. You will work closely with cross-functional teams to create cutting edge LLM-based experiences, make decisions using data and be responsible for developing innovative solutions that drive towards the future of AI in education. We are looking for enthusiastic and creative thinkers and doers to join us in meeting the challenge of shipping AI products that meet the needs of learners in a changing world.

Responsibilities:
Adopt, contribute to, and improve the teams existing technical stack and infrastructure to build differentiated features in the Education space.
Stay up to date with AI technology, advise on how we can adapt and embrace new technologies methodologies and ideas.
Provide hands-on technical work: design, analytics/modeling and fine tuning, dataset development, code reviews, development, quality assurance.
Build relationships with partners within our company (applied) Research Teams to bring innovation to new and existing products.
Foster a culture of inclusivity and disciplined data and software engineering practices to deliver business value, guided by data.
Creating and sustaining an effective team culture is extremely important to us. We value and promote a high trust environment where we work closely and effectively with others.
We emphasize a culture of continuous learning and so having a growth mindset and passion for learning is important. You should expect that your teammates will be invested in helping make you successful and expect the same in return from you. We recognize and celebrate the uniqueness of each person on the team and leverage our diversity to create more inclusive products for our customers.

Were in search of a Prompt Engineer with a Data Science background to join our Tel Aviv-based team and help shape our Large Language Model (LLM) capabilities. In this pivotal role, you will immerse yourself in the world of Large Language Models, diving deep into their intricate mechanisms and potentials. Through your work in shaping prompts and refining conversational AI performance, you'll play a crucial part in elevating the hiring experience for millions worldwide., you'll find an opportunity to explore the exciting frontier of conversational AI, directly influencing its evolution and application. Join us on this transformative journey and let's redefine the landscape of recruitment technology together!

Sound like your perfect fit? We want to hear from you!

As a Prompt Engineer, you will be responsible for developing and refining Large Language Model (LLM) prompts to strengthen and scale conversational AI capabilities. In this role, you will:

Dive deep into the potential of Large Language Models (LLMs), specifically aiming to enhance the conversation quality of our AI-driven hiring assistant. Experiment with different techniques and methodologies to optimize the interaction between users and AI.
Craft, test, refine, and optimize prompts to effectively communicate with existing AI models, aiming for improved clarity, efficiency, security and user satisfaction.
Implement and assess practical applications of LLM research findings in user-facing applications, creating scalable, impactful tools.
Develop scalable testing methods for LLMs, allowing for rigorous and systematic evaluation of different models, parameters, and techniques.
Work closely within our global R&D team to drive the evolution of prompt strategies, fostering a collaborative and innovative environment

We are looking for a Data Science Researcher - Finance and Marketing.
What you will be doing:
As a data scientist in the Business Data Science team, you will be leading finance and marketing projects with focus on time series handling.
Lead machine learning based projects from business requirements, through research, evaluation, production implementation and monitoring.
Work on diverse problems and domains, and work with several stakeholders on a daily basis including finance and marketing managers, developers, BI analysts and more.
Build ML and statistical models based on historical revenue streams and usage data to predict different KPIs. For example, LTV, campaigns ROI, users attribution and more.