• Define, analyze and validate hypotheses and models based on data • Work autonomously and in collaboration with business teams to deliver actionable insights • Generate added value for the business

Realize your potential by joining the leading performance-driven advertising company! As a Team Leader on the advertiser side ML group in our TLV Office, you’ll play a vital role in shaping how user intent is modeled at scale and how rich behavioral data is transformed into smarter recommendations. You will lead the design and evolution of large-scale Deep Learning models that directly improve advertiser performance and marketplace efficiency. By guiding team members and driving technical direction, you’ll turn modeling gaps into scalable production solutions. Your work will influence how millions of users discover content and how advertisers achieve measurable impact. To thrive in this role, you'll need: - Advanced academic training (MSc/PhD) with strong grounding in machine learning, optimization, and statistical modeling - Strong Machine Learning Modelling Experience (6+ years) - Production-Grade ML Engineering Experience - Managerial or Technical Leadership Experience - Experience Driving Measurable Business Impact Through Modeling - Experience leveraging agent-based coding tools to accelerate development, experimentation, and code quality in ML systems Bonus points if you have: Experience with Large-Scale Recommendation Systems How you’ll make an impact: - Lead the design and evolution of Taboola’s models - Own end-to-end ML delivery in production - Lead and develop ML engineers - Translate marketplace objectives into modeling strategy - Play a part of Taboola’s tech organization’s growth in the agentic development era Why Taboola? If you ask Taboolars what they love about working here, they’ll tell you that they’ve been empowered to realize their full potential while growing and learning from and with smart and talented people. They’ll also share more about: - Adam Singolda, Taboola Founder and CEO says; “You can copy anything from another business but you can’t copy a company’s culture. - Well-being: Enjoy comprehensive benefits (health, etc.), a fully stocked kitchen, and location-specific perks (gym partnerships, parking) - Flexibility: We offer a hybrid work schedule with 3 days in-office with an option to come in more often if desired. - Work with some of the biggest names: Our publisher partners include Yahoo, Conde Nast, Fox Sports, NBCU, ESPN, CBS, and E! Online. Our advertiser clients include Wells Fargo, Honda, Pinterest, Expedia and Honda. Ready to realize your potential? Taboola is an equal opportunity employer and we value diversity in all forms. We are committed to creating an inclusive environment for all employees and believe such an environment is critical for success. Employment is decided on the basis of qualifications, merit, and business need. Learn more about #TaboolaLife on LinkedIn, Facebook, Instagram, X, YouTube, & the Taboola Life Blog. About Taboola Taboola empowers businesses to grow through performance advertising technology that goes beyond search and social and delivers measurable outcomes at scale. Taboola works with thousands of businesses who advertise directly on Realize, Taboola’s powerful ad platform, reaching approximately 600M daily active users across some of the best publishers in the world. Publishers like NBC News, Yahoo, and OEMs such as Samsung, Xiaomi and others use Taboola’s technology to grow audience and revenue, enabling Realize to offer unique data, specialized algorithms, and unmatched scale. #LI-HP1 #LI-Hybrid

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary The Lead Data Scientist will play a critical role in developing, scaling, and deploying best‑in‑class forecasting models that drive merchandising category forecasting and decision‑making. This role sits within a high‑impact data science team responsible for translating complex transactional and behavioral data into accurate, actionable forecasts that inform financial, labor, and promotional strategies. The Lead Data Scientist will own end‑to‑end model development, from problem formulation and feature engineering through validation, deployment, and ongoing performance monitoring, while providing technical leadership and mentorship to other data scientists. Required Qualifications - 7+ years of professional experience in data science, applied statistics, or machine learning - Hands‑on experience developing time series forecasting or predictive models in a production environment - Proficiency in Python for data analysis and model development - Experience working with SQL and large, structured datasets - Demonstrated experience applying statistical or machine learning techniques (e.g., regression, tree‑based models, time series models) - Experience deploying, maintaining, or operationalizing models in partnership with engineering or analytics teams - Previous experience in a lead or senior individual contributor role, owning model design and technical decisions Preferred Qualifications - Strong problem‑solving skills with the ability to translate ambiguous business questions into well‑defined analytical problems - Experience influencing stakeholders and communicating model insights to non‑technical audiences - Background in finance, retail, or demand forecasting domains - Experience mentoring junior data scientists or providing technical leadership across projects - Familiarity with model monitoring, performance drift, and iterative model improvement - Ability to balance statistical rigor with pragmatic business constraints and timelines Education Bachelor’s degree or equivalent work experience in Mathematics, Statistics, Computer Science, Analytics, Engineering, or a related discipline. Master’s degree preferred. Pay Range The typical pay range for this role is: $130,295.00 - $260,590.00 This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above. This position also includes an award target in the company’s equity award program. Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong. Great benefits for great people We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families. This full‑time position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial well‑being of colleagues and their families. The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility. Additional details about available benefits are provided during the application process and on Benefits Moments. We anticipate the application window for this opening will close on: 05/31/2026 Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.

• Develop predictive and prescriptive models using machine learning techniques • Explore and analyze data to generate strategic insights • Build data pipelines and features for modeling • Collaborate with business areas to understand problems and propose data-driven solutions • Validate, test, and monitor models in production • Communicate results clearly to technical and non-technical stakeholders • Contribute to the evolution of data architecture and best practices.