Were looking for an experienced Product Analytics Manager to play a key role as part of the Product organization. The Product Analytics Manager will lead the domain and work closely with Product, R&D, Sales and Marketing, Customer Success, and BI to drive data-informed product and business decisions.
Key Responsibilities
Define product success metrics, usage benchmarks, and adoption KPIs across products and customer segments, ensuring alignment with clinical and business impact goals.
Own the end-to-end product analytics framework - including event design, tracking strategy, data validation, and ongoing data quality monitoring.
Perform hands-on deep-dive analyses, working directly with raw data and collaborating with R&D and BI to ensure robust data models, pipelines, and instrumentation.
Partner with Customer Success and BI to deliver customer-centric insights and support the evolution of the product data infrastructure.
Build dashboards and analytical models to support product and business decision-making, evaluate feature performance, and establish a continuous feedback loop between user behavior and product development - using data-driven insights to challenge assumptions and prioritize high-impact roadmap initiatives.
Quantify the clinical and economic impact of solutions by analyzing real-world encounter data to validate key performance markers such as reduction in cost of care, improved patient outcomes, and product utilization.
Translate complex analyses into clear, actionable recommendations for technical and non-technical stakeholders.
Provide professional leadership in analytics, including team management (if applicable) and contributing to the growth of the analytics function.

We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.

You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.

Responsibilities

Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Design, build, and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

We are seeking a hands-on technical leader to architect and lead the development of production LLM-based applications. This role will lead a cross-functional team of software engineers and data scientists and will own architecture, delivery, evaluation, observability, and production readiness.



The ideal candidate is a proven software leader who has delivered complex production-grade systems and can align engineering, data science, product, and platform work into one cohesive execution model. The role requires strong software architecture, applied AI judgment, disciplined execution, and people leadership, with a focus on turning ambiguous product goals and exploratory AI work into reliable production software.



Responsibilities:

Lead a cross-functional team of engineers and data scientists developing LLM-based applications.
Translate product and business goals into technical roadmaps, milestones, and deliverable plans.
Own end-to-end delivery from architecture and experimentation through deployment, monitoring, evaluation, and continuous improvement.
Design LLM workflows using RAG, tool calling, structured outputs, agents, deterministic logic, and human-in-the-loop patterns where appropriate.
Establish evaluation-driven development practices for LLM features.
Define quality metrics, regression tests, golden datasets, production feedback loops, and observability standards.
Ensure systems meet production standards for scalability, performance, reliability, security, and maintainability.
Provide technical guidance, architectural oversight, mentorship, and career development for team members.
Manage priorities, scope, risks, and trade-offs across multiple streams of work.
Partner with product, design, DevOps, data engineering, security, and other technical leaders.
Drive continuous improvement in delivery quality, engineering practices, and team effectiveness.


We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.

Responsibilities:

Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.
Requirements

We are looking for a Senior Data Scientist to join a team of passionate engineers who build premium solutions at scale. Every day, we tackle ambiguous and stimulating challenges. We build and operate large-scale distributed machine learning systems that process 1B predictions per second during real-time auctions.

What will you do?
As a Senior Data Scientist, your mission will be to:

Improve existing algorithms & tools allowing to explore and analyze more and more data and provide accurate feedback on the Teads activities.
Develop new algorithms & new approaches to provide accurate predictions and drive new products by providing business insights.
Implement your algorithms and models end to end.
Collaborate with a variety of teams to develop services from design to production.
Make sure the software is in good hands by writing, running and automating tests (unit, functional, load...).
Keep up to date with the latest Machine Learning technologies to make sure we always use the best class algorithms according to the context.
Shape how billions of people discover and enjoy premium content by improving ad relevance, quality, and efficiency across Teads global publisher network.