דרושים » מדעים מדוייקים » data Science בחברת Ad-Tech

We are looking for a Machine Learning Tech Lead to promote machine learning engineering excellence. Someone who is passionate about building scalable, high-quality data products and processes, while ensuring production systems maintain strong real-time performance observability.

As a Tech Lead on the ML team , you will focus on designing and maintaining the core infrastructure that empowers the Machine Learning Engineers working within Data Science product teams. Youll collaborate closely with stakeholders across data science, product, and engineering, playing a pivotal role in driving the business by architecting and enabling the infrastructure for machine learning model development, serving, and lifecycle managementthe foundation of our product.

Responsibilities:

Partner with MLEs in Data Science product teams and key stakeholders to design and maintain infrastructure for:
Data wrangling supporting and enabling data requirements for research, training, validation, and testing.
End-to-end ML delivery enabling model performance development, training, validation, testing, and version control.
Drive engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.
Build and support monitoring and observability tools dashboards, alerts, and performance tracking of models in production.
Lead architecture projects such as:
Feature Store centralizing feature engineering and serving across teams.
Vector Databases enabling large-scale embedding storage and retrieval for advanced ML applications.
GPU Cluster Scaling optimizing distributed training and inference infrastructure.
Collaborate with product, data science, and engineering teams to solve complex problems, identify trends, and create opportunities through robust ML infrastructure.

We're looking for a skilled and motivated Data Science Team Lead to guide a team of talented data scientists in developing real-time, machine learning-based products. Youll play a key role in driving innovation - researching, designing, and deploying advanced ML models that directly enhance user experience and product performance.

This is an exciting opportunity for a hands-on leader who thrives in a fast-paced, impact-driven environment.

Responsibilities:

Lead, mentor, and inspire a team of data scientistsfostering a culture of collaboration, innovation, and technical excellence
Guide team members personal and professional development
Collaborate with product and engineering teams to identify challenges, uncover opportunities, and drive data-driven solutions
Recruit and retain top-tier talent as the team grows
Research and develop ML models using cutting-edge techniques across a range of domains
Generate new ideas and insights to improve performance and deliver business value

We are looking for an experienced Data Science Manager to work on incredibly interesting projects as we take our personalization capabilities to the next level. You will focus on leading the team that are developing and advancing ML/AI across our platforms, researching and investigating new machine learning applications within the company, and improving pre-existing models.

Role & Core Responsibilities
Lead, mentor, motivate and inspire a small team of Machine Learning Engineers & Data Scientists.
Own the model development and release process across all products and internal platforms, including both OptiX and .
Manage the cloud-hosted modelling environment.
Operationalize models as APIs working in real-time and batch environments.
Monitor production models, ensuring data quality and model performance.
Develop predictive machine learning models for classification, ranking, and personalization purposes, utilizing multi-modal data including images and text.
Leverage LLMs and other cutting-edge technologies to enhance product capabilities.
Collaborate closely with product and development teams to define and prepare new ML applications.
Analyse performance and continuously improve scoring processes for hosted models.