לוח משרות דרושים מנהל מחלקת Data משרה מלאה, לחיפוש זה נמצאו 18 משרות

Prisma-Photonics is a rapidly growing startup company, developing the next-generation smart-infrastructure solution based on novel fiber-sensing technology (smart roads, smart cities, perimeters, and grid monitoring, etc.). The company offers an award-winning disruptive solution; a “sensor free” approach to smart infrastructure. The company is VC backed and in the revenues stage. Combining pioneering technology in optical fiber sensing with state-of-the-art machine learning, we help prevent environmental disasters, protect human lives, and keep critical energy and transportation backbones running smoothly. We are seeking our Data Operations Team Leader! This person will oversee a team of data operations specialists responsible for labeling data for machine learning and AI projects. This role involves managing day-to-day operations, ensuring annotation quality and accuracy, developing guidelines, and collaborating with cross-functional teams to meet project deadlines and objectives.

Hiring Manager:
Guy Engel

What You’ll Do:

* Manage the end-to-end data workflow, from incoming data through annotation delivery to the ML teams, including overseeing data upload processes and ensuring data availability.
* Manage database operations and ensure the integrity and availability of annotation data.
* Become a Subject Matter Expert in our data and actively participate in hands-on annotation projects.
* Create and maintain onboarding materials, annotation guidelines, and operational documentation to support knowledge sharing and consistent annotation practices.
* Lead a team of Data Operations Specialists by managing task assignments, scheduling, and day-to-day execution. Provide the tools and processes needed for efficient work, ensure high annotation quality in collaboration with the ML teams, and continuously improve workflow efficiency
* Own the annotation platform, driving bug fixes, feature development, and workflow improvements using Python and GenAI tools.
* Develop and maintain operational dashboards and real-time performance metrics to monitor team efficiency, quality, and output.
* Collaborate closely with data scientists, machine learning engineers, and product managers to understand annotation requirements and project goals. Coordinate cross-functional communication, monitor operational KPIs, and oversee daily operations to keep projects on track and ensure adherence to shifting schedules.

What You Bring:

* STEM graduate or equivalent Technical background
* Experience leading a team of Data Operations Specialists or similar teams.
* Proven ability to manage multiple projects simultaneously and effectively prioritize competing responsibilities.
* Excellent communication and collaboration skills, with a demonstrated ability to build strong cross-functional relationships and foster team success.
* Experience developing internal tools and micro-services with Python, and leveraging GenAI tools to accelerate development and operational workflows.


Great to Have:

* Experience leading juniors and/or part-time students.
* Experience with seismic data and/or spectrograms.
* Experience designing, developing, or improving annotation tools and annotation workflows.

As a Data Science Manager, you will lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions. You will own the end-to-end development lifecycle of AI projects, from exploratory research and experimentation through to scalable deployment and optimization. In this role, you will partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery. You will also drive innovation across a broad spectrum of ML domains, including supervised/unsupervised learning, anomaly detection, and generative AI, while providing hands-on guidance and championing best practices in MLOps, data governance, and model evaluation.
Key Responsibilities
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.

Were looking for a Data & Analytics Team Lead to manage and grow our data team and own the data layer behind our AI. Youll lead a team of data analysts responsible for neural-network model analysis, model performance tracking, customer-facing and internal BI. Youll act as both the people manager and the technical lead for the team, setting technical direction while staying hands-on.

Youll thrive in this role if youre passionate about turning data into better AI products, enjoy solving complex analytical problems, and like working at the intersection of Data, Analytics, and Artificial Intelligence.

Responsibilities:

Lead the data strategy supporting AI model development, evaluation, and continuous improvement
Define KPIs and monitoring frameworks for AI model performance.
Analyze NN model outputs, build and maintain model-tracking and performance-evaluation workflows
Drive close calibration with the Algo, Product, and Data Engineering teams to align data definitions, pipelines, and metrics
Stay hands-on with SQL, Python, BI tooling, and the modern data stack
Build and own agentic data workflows - designing the context, tooling, and data access that let AI agents reliably query, analyze, and act on company data