לוח משרות דרושים Data Scientist בפתח תקווה

We are seeking a hands-on Tech Lead to be the first technical hire for our newly formed ML team. This is a unique ground-floor opportunity to build something from scratch and have direct influence on the technical direction of our ML capabilities. This role combines deep technical expertise with foundational leadership, requiring someone who can architect solutions, establish technical standards, and lay the groundwork for future team growth. As our founding ML tech lead, you'll have the opportunity to shape our technology choices, define our technical practices, and build the fundamental skill set that will guide the team as we scale.
What youll do: 
Shape the technical foundation: As our first ML hands on hire, choose and establish the team's core technology stack, frameworks, and development practices together with the ML leader
Build from the ground up: Design and implement our initial ML infrastructure, data pipelines, and model deployment frameworks
Define technical standards: Establish coding standards, best practices, and processes that will guide future team members
Hands-on development: Lead by example through direct development and deployment of ML models and data science solutions
Partner with stakeholders: Work closely with business teams to identify initial ML opportunities and build foundational proof-of-concept models
Establish MLOps practices: Set up our model development, testing, deployment, and monitoring workflows from scratch
Recruit and onboard: As the team grows, play a key role in technical interviews and help onboard new ML engineers and data scientists
Create team culture: Help establish the team's technical culture, learning practices, and collaboration methods
Technology evaluation: Research and select the tools, platforms, and technologies that will form our ML tech stack
Document and share knowledge: Create foundational documentation and knowledge-sharing practices for the growing team.

What will you do:
As Senior Data scientist at our company, you will shape the future of identity security and develop innovative AI/ML solutions that enhance security and protect the emerging agentic AI landscape in top organizations. Drive AI research and engineering to address evolving threats and protect critical digital assets effectively.
Drive AI/ML projects from research to production, leveraging advanced algorithms, LLMs, classification models, Unsupervised learning and optimization techniques to enhance cybersecurity solutions.
Model Building: Design, implement, and optimize machine learning models, including deep learning, using frameworks such as PyTorch, TensorFlow, Hugging Face, and scikit-learn.
Work closely with product manager, engineers, software architects and analysts to understand requirements, validate model assumptions, and deliver actionable insights.
Identify AI/ML impact opportunities and translate business needs into actionable roadmap items.
Stay current with the latest advancements in data science and apply best practices to ensure optimal performance.

As a Data Science Team Leader at our company, you will lead a high-performing team developing AI/ML solutions that secure the digital worlds most sensitive assets. Youll play a key role in driving innovation, productizing machine learning systems, and applying advanced modeling techniques to real-world cybersecurity challenges. In this leadership role, youll balance hands-on technical involvement with strategic oversight, guiding projects from research to production. Youll work closely with data scientists, engineers, analysts, product managers, and Cyber-Security researchers to build intelligent features across our copmpanys identity security portfolio.
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.