Cint is a pioneer in research technology (ResTech). Our customers use the Cint platform to post questions and get answers from real people to build business strategies, confidently publish research, accurately measure the impact of digital advertising, and more. The Cint platform is built on a programmatic marketplace, which is the world’s largest, with nearly 300 million respondents in over 150 countries who consent to sharing their opinions, motivations, and behaviours.

We are feeding the world’s curiosity!

Job Description

Role Overview

As a Principal Data Scientist at Cint, you will lead advanced analytics and data science initiatives for Data Solutions and Media Measurement product lines. This role involves using statistical modeling, machine learning, large language models and advanced data analysis techniques. Ultimately, you will be accountable for developing and maintaining research methods that align Cint capabilities with market claims and industry standards of measurement. The ideal candidate will possess strong technical and thought leadership skills and will be comfortable working cross-functionally with product, engineers, and business stakeholders.

Key Responsibilities:

1. Develop and deploy statistical models, machine learning algorithms, and custom analytics solutions to measure the effectiveness of media campaigns.
2. Collaborate with cross-functional teams to design, refine and automate measurement methodologies for TV, digital, social, and other media platforms.
3. 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.
4. Develop customer-facing methodology resources and thought leadership. Support business teams in explaining and defending our measurement methodologies broadly to customers.
5. Independently plan, develop, and manage projects from concept to completion with no supervision, ensuring timely and high-quality delivery.
6. Partner with product teams and other stakeholders to translate business needs into actionable data science initiatives.
7. Serve as a technical leader and mentor to other data scientists in the team, promoting best practices in coding, experimentation, and analytical techniques.
8. Stay updated with the latest industry trends and emerging methodologies in media measurement and data science.
9. Communicate complex results, insights and strategic recommendation to non-technical audiences through compelling data visualizations, detailed reports and presentations.

Qualifications

Qualifications:

1. Advanced degree (Ph.D. or Masters) in a quantitative field such as Data Science, Statistics, Mathematics, Economics, or Computer Science with outstanding analytical expertise.
2. 10+ years of experience in data science, analytics, or related fields, with at least 4 years focused on media measurement, marketing analytics, or advertising.
3. Proven track record in leading large-scale data science projects, mentoring teams, driving strategy and delivering business impact.
4. Extensive experience in advanced statistical techniques and concepts (e.g., multivariate (parametric/ non-parametric) testing, sampling theory, weighting/projection, experimental design, regression/predictive modeling, causal inference techniques).
5. Strong programming skills in Python (as statistical and ML package tools).
6. Proven expertise in advanced Python prototyping.
7. Proficiency with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and techniques (e.g., clustering, regression, decision-trees).
8. Advanced SQL skills and familiarity with big data technologies (Spark, Hadoop, Databricks).
9. Strong understanding of infrastructure cost management, particularly in relation to processing large datasets efficiently.

Preferred Qualifications:

1. Experience in online survey methodologies.

Personal Attributes:

1. Excellent communication skills and advanced presentation skills, with the ability to explain complex technical concepts to a large technical and non-technical audience.
2. Excellent interpersonal skills, able to work effectively with cross-functional teams and stakeholders.
3. Strong ability to analyze complex and large data sets and extract meaningful insights.
4. A genuine interest in exploring new data science methods, tools, and technologies to create and implement innovative solutions and approaches.

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