Ipsos in US
Senior Data Scientist, Creative Excellence
Ipsos in US, San Francisco, California, United States
Job Description
Data Scientist, Creative Excellence – Ipsos (US)
Within Ipsos, the Creative Excellence team helps clients understand what makes advertising effective across TV, digital, social and other media channels. A core strategic solution is Creative Spark AI, an AI‑enabled capability to predict and explain ad performance at scale and globally. Your primary focus will be to support the development and evolution of this solution and to create new products that leverage the framework for emerging opportunities.
What We Offer
The opportunity to work with cutting‑edge AI and data technologies in a production environment, alongside other experienced data scientists and engineers.
A culture that values curiosity, scientific rigor, collaboration, and continuous learning.
The chance to grow towards more senior or specialized roles (e.g. lead data scientist, AI product specialist, or domain expert in creative analytics).
Role Summary In this role, you will contribute to the design and experimentation of the AI models and features that power Creative|Spark AI and related solutions. When client and industry needs arise, you will experiment to discover alternative measurement and modelling best practices that may become independent products or solutions. You will translate research and client briefs into modelling problems, ensuring actionable insights and recommendations. You are expected to work autonomously on well‑defined problems, collaborate closely with lead data scientists and engineers, and progressively take ownership of more complex modelling workstreams.
Key Responsibilities
Feature and Model Development
Design, engineer, and test new model variants from survey, coded, or digital data sources to improve prediction accuracy and explainability of ad performance across distinct ad environments and verticals.
Integrate new features into experimental models and quantify their impact on prediction accuracy, robustness, and interpretability. Summarize the uplift (or lack thereof) for senior management decision‑making.
Document feature definitions, derivation logic, and performance impact for replicability.
Experimentation, Evaluation & Documentation
Design and execute experiments and benchmarks comparing different feature sets, algorithms, or model configurations (e.g. classical ML, deep learning, NLP/CV approaches).
Use appropriate evaluation metrics (accuracy, AUC, RMSE, calibration, stability across segments) and validation schemes (cross‑validation, hold‑out, time‑based splits) to ensure robust conclusions.
Maintain clear experiment logs and documentation (notebooks, reports, dashboards) so results can be reviewed, reproduced, and reused by CRE and GADS teams.
Contribute to continuous improvement of modelling best practices for Creative|Spark AI.
Product and Revenue Growth
Act as an advocate for, and owner of, new products and solutions that generate incremental revenue on top of CRE’s core business.
Develop an understanding of Ipsos’ Creative Excellence business and its evolution into new spaces. Translate this understanding into new solutions with global and U.S. product teams, answering client questions consistently, efficiently and accurately using ML, Gen‑AI and survey methods.
Drive the transformation of Ipsos’s business model through the strategic use of synthetic data, enhancing insight generation and enabling advanced market simulations.
Support innovative product development, new revenue streams, and greater value from data assets.
Communication & Stakeholder Engagement
Help translate stakeholder business and research questions into robust, documented analytical workflows aligned with Ipsos’ methodologies and AI governance.
Present modelling results, feature impacts, and recommendations in clear, non‑technical language to CRE stakeholders.
Collaborate with business‑facing teams to frame and refine client questions, ensuring feasibility and methodological rigor.
Contribute to internal training, playbooks, and knowledge sharing on our core AI solution.
Support client‑facing presentations or proposals with concise, well‑structured analytical inputs when relevant.
Skills & Qualifications Education
Master’s degree (or equivalent) in Data Science, Statistics, Applied Mathematics, Computer Science, Econometrics, or a related quantitative field.
Ph.D. is a plus but not required for this role.
Experience
7–10 years of professional experience as a Data Scientist in applied machine learning.
Hands‑on experience building and evaluating supervised learning models (regression / classification) in real‑world use cases.
Experience in product management or technical lead roles is a plus.
Experience in at least one of the following: marketing, advertising, media, or market research, or predictive modelling on survey, panel, or customer behaviour data.
Prior exposure to production or near‑production environments (e.g. working on models that are deployed, monitored, and iterated).
Technical Skills
Computer Vision, GenAI, and NLP.
Strong proficiency in Python and the main data & ML libraries (pandas, NumPy, scikit‑learn, plus optionally TensorFlow / PyTorch / CatBoost / XGBoost).
Good working knowledge of SQL and experience querying large analytical datasets (e.g. in BigQuery or similar cloud warehouses).
Demonstrated understanding of core ML concepts: Feature engineering, regularization, model selection, cross‑validation.
Evaluation metrics for regression / classification.
Bias, overfitting, drift, and robustness issues.
Experience with the following: NLP or Computer Vision applied to creatives (scripts, storyboards, video / image / audio).
Cloud platforms, ideally Google Cloud Platform (GCP).
Experiment tracking and MLOps tools (e.g. MLflow, model registries, CI/CD for ML).
Strong analytical and problem‑solving skills, with attention to detail and methodological rigor.
Ability to understand business and research problems and translate them into concrete analytical approaches.
Comfortable working in cross‑functional teams (data science, engineering, research, client service).
Curious, pragmatic, and eager to learn from both technical and non‑technical colleagues.
Able to work autonomously on clearly defined workstreams, while actively seeking feedback when needed.
Excellent communication skills.
Benefits
Career development opportunities.
Exceptional benefits package (generous PTO, healthcare plans, wellness benefits).
Flexible workplace policy.
Strong collaborative culture.
Commitment to Diversity Ipsos recognizes the necessity of building an inclusive culture that values each employee’s individuality and diverse perspectives. Ipsos is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or any other protected class and will not be discriminated against on the basis of disability.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Engineering and Information Technology
Industries
Market Research
#J-18808-Ljbffr
Within Ipsos, the Creative Excellence team helps clients understand what makes advertising effective across TV, digital, social and other media channels. A core strategic solution is Creative Spark AI, an AI‑enabled capability to predict and explain ad performance at scale and globally. Your primary focus will be to support the development and evolution of this solution and to create new products that leverage the framework for emerging opportunities.
What We Offer
The opportunity to work with cutting‑edge AI and data technologies in a production environment, alongside other experienced data scientists and engineers.
A culture that values curiosity, scientific rigor, collaboration, and continuous learning.
The chance to grow towards more senior or specialized roles (e.g. lead data scientist, AI product specialist, or domain expert in creative analytics).
Role Summary In this role, you will contribute to the design and experimentation of the AI models and features that power Creative|Spark AI and related solutions. When client and industry needs arise, you will experiment to discover alternative measurement and modelling best practices that may become independent products or solutions. You will translate research and client briefs into modelling problems, ensuring actionable insights and recommendations. You are expected to work autonomously on well‑defined problems, collaborate closely with lead data scientists and engineers, and progressively take ownership of more complex modelling workstreams.
Key Responsibilities
Feature and Model Development
Design, engineer, and test new model variants from survey, coded, or digital data sources to improve prediction accuracy and explainability of ad performance across distinct ad environments and verticals.
Integrate new features into experimental models and quantify their impact on prediction accuracy, robustness, and interpretability. Summarize the uplift (or lack thereof) for senior management decision‑making.
Document feature definitions, derivation logic, and performance impact for replicability.
Experimentation, Evaluation & Documentation
Design and execute experiments and benchmarks comparing different feature sets, algorithms, or model configurations (e.g. classical ML, deep learning, NLP/CV approaches).
Use appropriate evaluation metrics (accuracy, AUC, RMSE, calibration, stability across segments) and validation schemes (cross‑validation, hold‑out, time‑based splits) to ensure robust conclusions.
Maintain clear experiment logs and documentation (notebooks, reports, dashboards) so results can be reviewed, reproduced, and reused by CRE and GADS teams.
Contribute to continuous improvement of modelling best practices for Creative|Spark AI.
Product and Revenue Growth
Act as an advocate for, and owner of, new products and solutions that generate incremental revenue on top of CRE’s core business.
Develop an understanding of Ipsos’ Creative Excellence business and its evolution into new spaces. Translate this understanding into new solutions with global and U.S. product teams, answering client questions consistently, efficiently and accurately using ML, Gen‑AI and survey methods.
Drive the transformation of Ipsos’s business model through the strategic use of synthetic data, enhancing insight generation and enabling advanced market simulations.
Support innovative product development, new revenue streams, and greater value from data assets.
Communication & Stakeholder Engagement
Help translate stakeholder business and research questions into robust, documented analytical workflows aligned with Ipsos’ methodologies and AI governance.
Present modelling results, feature impacts, and recommendations in clear, non‑technical language to CRE stakeholders.
Collaborate with business‑facing teams to frame and refine client questions, ensuring feasibility and methodological rigor.
Contribute to internal training, playbooks, and knowledge sharing on our core AI solution.
Support client‑facing presentations or proposals with concise, well‑structured analytical inputs when relevant.
Skills & Qualifications Education
Master’s degree (or equivalent) in Data Science, Statistics, Applied Mathematics, Computer Science, Econometrics, or a related quantitative field.
Ph.D. is a plus but not required for this role.
Experience
7–10 years of professional experience as a Data Scientist in applied machine learning.
Hands‑on experience building and evaluating supervised learning models (regression / classification) in real‑world use cases.
Experience in product management or technical lead roles is a plus.
Experience in at least one of the following: marketing, advertising, media, or market research, or predictive modelling on survey, panel, or customer behaviour data.
Prior exposure to production or near‑production environments (e.g. working on models that are deployed, monitored, and iterated).
Technical Skills
Computer Vision, GenAI, and NLP.
Strong proficiency in Python and the main data & ML libraries (pandas, NumPy, scikit‑learn, plus optionally TensorFlow / PyTorch / CatBoost / XGBoost).
Good working knowledge of SQL and experience querying large analytical datasets (e.g. in BigQuery or similar cloud warehouses).
Demonstrated understanding of core ML concepts: Feature engineering, regularization, model selection, cross‑validation.
Evaluation metrics for regression / classification.
Bias, overfitting, drift, and robustness issues.
Experience with the following: NLP or Computer Vision applied to creatives (scripts, storyboards, video / image / audio).
Cloud platforms, ideally Google Cloud Platform (GCP).
Experiment tracking and MLOps tools (e.g. MLflow, model registries, CI/CD for ML).
Strong analytical and problem‑solving skills, with attention to detail and methodological rigor.
Ability to understand business and research problems and translate them into concrete analytical approaches.
Comfortable working in cross‑functional teams (data science, engineering, research, client service).
Curious, pragmatic, and eager to learn from both technical and non‑technical colleagues.
Able to work autonomously on clearly defined workstreams, while actively seeking feedback when needed.
Excellent communication skills.
Benefits
Career development opportunities.
Exceptional benefits package (generous PTO, healthcare plans, wellness benefits).
Flexible workplace policy.
Strong collaborative culture.
Commitment to Diversity Ipsos recognizes the necessity of building an inclusive culture that values each employee’s individuality and diverse perspectives. Ipsos is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or any other protected class and will not be discriminated against on the basis of disability.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Engineering and Information Technology
Industries
Market Research
#J-18808-Ljbffr