Staff Data Scientist, Cint

• Advanced degree (Ph.D. or Master's) in a quantitative field such as Data Science, Statistics, Mathematics, Operations Research, Economics, Computer Science, or Quantitative Sciences with outstanding analytical expertise
• 7+ years of experience in data science and analytics, machine learning, model development/validation, or related fields, with at least 2 years focused on media measurement, marketing analytics, or advertising
• Proven track record in leading large-scale data science projects, mentoring teams, driving strategy and delivering business impact
• Demonstrated experience in advanced statistical techniques and concepts
• E.g., properties of distributions, hypothesis testing, multivariate (parametric/ non-parametric) testing, sampling theory, weighting/projection, experimental design, regression/predictive modeling, causal inference techniques, stochastic modeling / simulation, and more
• Strong programming skills in Python (as it relates to statistical analysis and implementing Machine Learning models)
• Proven expertise in advanced Python prototyping
• Proficiency with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and techniques (e.g., clustering, regression, tree-based models)
• Advanced SQL skills and familiarity with big data technologies (Spark, Hadoop, Databricks)
• Demonstrated ability to independently and confidently carry out projects end-to-end
• Understanding of infrastructure cost management, particularly in relation to processing large datasets efficiently
• Excellent communication skills and advanced presentation skills, with the ability to explain complex technical concepts to large technical and non-technical audiences
• Excellent interpersonal skills, ability to work effectively with cross-functional teams and stakeholders
• Strong ability to analyze complex and large data sets and extract meaningful insights
• A genuine interest in exploring new data science methods, tools, and technologies to create and implement innovative solutions and approaches

Desirable

• As a Staff Data Scientist at Cint, you will be responsible for designing and developing advanced statistical and machine learning methodologies across our Media Measurement and Data Solutions product lines
• You will lead high-impact initiatives, guide cross-functional collaborations, and serve as a subject matter expert on data science methodologies that support our product suite
• You are expected to work independently across multiple complex projects, help shape data science strategy, mentor and coach team members, and translate business challenges into scalable, data-driven solutions
• Your work will ensure that our data science solutions are robust, scalable, and aligned with both market demands and company objectives
• Develop and deploy statistical models, machine learning algorithms, and custom analytics solutions to measure the effectiveness of media campaigns
• Collaborate with cross-functional teams to design, refine and automate measurement methodologies for TV, digital, social, and other media platforms
• Lead the research and discovery phases for both new and existing products and partner closely with engineering teams to transition prototypes into robust, scalable solutions
• Develop customer-facing methodology resources and thought leadership. Support business teams in explaining and defending our measurement methodologies broadly to customers
• Plan, develop, and manage projects from concept to completion with no supervision, ensuring timely and high-quality delivery
• Partner with product teams and other stakeholders to translate business needs into actionable data science initiatives
• Serve as a technical leader and mentor to other data scientists in the team, promoting best practices in coding, experimentation, and analytical techniques
• Stay up to date with the latest industry trends and emerging methodologies in media measurement and data science
• Communicate complex results, insights and strategic recommendations to both technical and non-technical audiences through compelling data visualizations, detailed reports and presentations