Senior Data Scientist
Pubblicato il 17-08-2026 - Factored in Modena
Fully remote | Complete engagement job
Founded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world.
At Factored, you’ll be part of a community that values learning, ownership, and authenticity, where your growth is personal and your ideas matter. We’re transparent, curious, and collaborative. We strive for excellence, celebrate diversity, encourage curiosity, and build an environment where you can truly thrive.
We are currently looking for an exceptionally talented Senior Data Scientist to join our team. You will be responsible for extracting insights from complex data sets, developing recommender systems, and applying advanced analytical techniques to drive strategic decision-making and optimize business outcomes.
Functional Responsibilities:
Designing models with a user-centric output,
such as recommender systems tailored for various applications including offer design, consumer behavior prediction for purchases, and churn prediction, among other functionalities.
Conduct exploratory data analysis to uncover hidden patterns and extract valuable insights that contribute to business growth and optimization.
Utilize predictive modeling and forecasting techniques to support decision-making processes and improve operational efficiency.
Continuously monitor model performance, assess data quality, and refine models to ensure accuracy and reliability.
Stay up to date with the latest advancements in data science and technology, and identify opportunities to apply emerging methodologies to enhance existing models and processes.
Qualifications:
Minimum of 4 years of professional experience in Data Science, Machine Learning, or a related field.
Strong proficiency in programming languages such as SQL, Python and experience with relevant libraries