Lead Data Scientist

Company Description HummingBird is a Responsible AI-powered total talent workforce platform that transforms how organizations attract, engage, and retain talent through a seamless, personalized, and data-driven experience. The platform optimizes every stage of the talent lifecycle from sourcing and pre-screening to hiring, referrals, and ongoing engagement using advanced AI and predictive insights. With robust capabilities such as AI-generated job descriptions, intelligent candidate analysis, customizable scoring models, and pre-built and custom analytics reports, HummingBird enables data-driven decision-making at scale. The system integrates with major ATS and VMS solutions, offers white-labeled branding, and provides access to over 20 million global talents, creating a unified recruitment ecosystem. Its powerful filtering, segmentation, and automation tools help employers improve operational efficiency and deliver consistently high-quality candidate experiences.

Role Description This full-time, on-site role is based in New York, NY and focuses on using data science and analytics to enhance HummingBird’s talent intelligence and platform performance. Data Scientists & Analysts in this role will collect, clean, and analyze large datasets from multiple sources, develop statistical and machine learning models, and generate insights that improve candidate matching, scoring, and engagement journeys. Day-to-day responsibilities include building dashboards and visualizations, conducting exploratory and performance analyses, and collaborating with product, engineering, and customer success teams to translate business requirements into data solutions. The role also involves designing experiments, evaluating algorithmic changes, and contributing to predictive forecasting models that support strategic workforce decisions. Team members are expected to document methodologies, present findings to stakeholders, and continuously seek opportunities to refine metrics and enhance the platform’s data-driven capabilities.

Qualifications

• Strong foundation in Data Science, including experience with machine learning models, feature engineering, and algorithm evaluation.
• Applied knowledge of Statistics for hypothesis testing, experimental design, and probabilistic modeling.
• Proficiency in Data Analytics and Data Analysis, including data cleaning, transformation, and interpretation of complex datasets.
• Experience with Data Visualization using tools such as Tableau, Power BI, or similar platforms, and libraries like matplotlib, ggplot, or Plotly.
• Skill with programming languages commonly used in analytics and data science, such as Python or R, and familiarity with SQL for querying relational databases.
• Background in building dashboards and reports that support data-driven decision-making for product, operations, or talent-related initiatives.
• Bachelor’s or master’s degree in a quantitative field (e.g., Data Science, Statistics, Computer Science, Mathematics, Economics, or related discipline), or equivalent practical experience.