Company Description
Who We Are
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
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.
Key Responsibilities
Qualifications
Qualifications:
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.
Preferred Qualifications
Additional Information
Personal Attributes:
Additional information
Anticipated Salary Range (US Only)
In California, Colorado, New York City and Washington, the anticipated pay range for this role is $167,200 to $200,000 annual base salary. This base pay range is specific to California, Colorado, New York City and Washington and may not be applicable to other locations. In addition, this position is also eligible for the following benefits:
Our Values
Collaboration is our superpower
Innovation is in our blood
We Do What We Say
We are caring
More About Cint
We’re proud to be recognised in Newsweek’s 2025 Global Top 100 Most Loved Workplaces®, reflecting our commitment to a culture of trust, respect, and employee growth.
In June 2021, Cint acquired Berlin-based GapFish – the world’s largest ISO certified online panel community in the DACH region – and in January 2022, completed the acquisition of US-based Lucid – a programmatic research technology platform that provides access to first-party survey data in over 110 countries.
Cint Group AB (publ), listed on Nasdaq Stockholm, this growth has made Cint a strong global platform with teams across its many global offices, including Stockholm, London, New York, New Orleans, Singapore, Tokyo and Sydney. (www.cint.com)