Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
Type: Remote, Full-time
About the role
You will build and validate the models that define, expand, and score the audiences advertisers activate across TV, CTV, digital, social, and programmatic. Working across survey, purchase, and cross-platform media data, you turn messy multi-source signals into accurate, explainable, privacy-safe segments, and you help shape the product itself with ideas for new audience capabilities. You will partner closely with ML Engineers and the US product and analytics team.
What you'll do
Build statistical and ML models to create, expand, and score audience segments from survey, panel, purchase, and media-exposure data.
Develop propensity and lookalike models that scale small seed audiences to addressable populations.
Lead data fusion work, combining deterministic and probabilistic sources into one representative consumer view while correcting for bias.
Ideate on making the product better: propose new audiences, features, and methods, and prioritize them with the US team.
Own audience measurement analytics: reach, overlap, index strength, and incremental lift (A/B, matched control, causal inference).
Partner with ML Engineering to move models into reproducible, monitored production, under privacy-by-design principles.
Must have
~5+ years in applied data science, with models that reached production or client delivery.
Sound statistical modeling (survey stats, causal inference, experimental design, propensity/uplift).
Working knowledge of Databricks; comfortable in a Spark / Snowflake environment.
Strong Python (pandas, scikit-learn, statsmodels) and SQL against large databases.
Degree in a quantitative field (Statistics, Data Science, Economics, CS, Math, or similar).
Nice to have
Media / advertising background (strong plus).
Audience/identity or ad tech exposure (DSP/SSP, DMP/CDP, clean rooms, identity graphs).
Familiarity with privacy-preserving methods and GDPR/CCPA in data collaboration.
