Principal Data Scientist, Predictive Modeling

At Nielsen, we believe that career growth is a partnership. You ultimately own, fuel, and set the journey. By joining our team of nearly 14,000 associates, you will become part of a community that will help you to succeed. We champion you because when you succeed, we do too. Embark on a new initiative, explore a fresh approach, and take license to think big so we can all continuously improve. We enable your best to power our future. Principal Data Scientist, Predictive Modeling What would make me a great candidate? Enjoy applying skills to developing new models and processes for predictive modeling. Hands‑on in creating models and capable of overseeing the work of others as they apply the tools to additional datasets. Familiar with survey research and recommendation engine models. Highly analytical and able to defend model validity by conducting hold‑out analyses, presenting results in plain language to non‑data‑science audiences. Passionate about continuous improvement, driving operational efficiency in modeling speed and compute usage. Excited to build new capabilities and help drive advancement of the business. Responsibilities Design, test, and implement recommendation engine models to turn sparse datasets into synthetic datasets, writing code from scratch. Proficiently read, manipulate, and analyze big data and build models. Validate models through analytic research (hold‑outs) and communicate predictive power internally and to clients as needed. Use iterative modeling to determine ideal parameters for sparse data—minimum level of completeness, key data points for higher predictive accuracy, minimum number of data points for a given predicted variable, and similar metrics. Consult with internal and external partners on the best way to source the ideal sparse data based on analytic findings. Support screening, hiring, onboarding, and development of mid and junior data scientists. Act as subject‑matter expert for internal and external stakeholders. Provide technical assistance in predictive modeling methodologies, data manipulation, fusion, and modeling. Define and implement a vision for scaling new models across increasingly larger datasets, driving efficient speed and computer usage. Support automation for scaling routinized processes. Support pilot programs for R&D purposes. Conduct tactical or strategic analyses to address business and customer opportunities. Utilize tools such as Python, R, SPSS, etc., to perform complex data analysis and develop automation procedures. Develop, test, and implement high‑quality, modular Python code that integrates into existing production systems. Implement machine‑learning solutions leveraging big data from internal and external sources. Assist with ad hoc analyses and projects. Qualifications Undergraduate or graduate degree in Mathematics, Statistics, Social Science, Engineering, Computer Science, Economics, Business or related fields employing rigorous data analysis and strong statistical skills. 10+ years of relevant data‑science experience implementing various types of modeling. Prior experience with predictive modeling and specifically with recommendation engines is highly preferred. Strong skills in Python and relevant packages, including PyTorch, and familiarity with other scripting languages. Knowledge of statistical tests and procedures such as correlation, regression, hypothesis testing, segmentation techniques, ANOVA, chi‑squared, Student‑t test, and time series. Familiarity with machine‑learning and data modeling techniques such as decision trees, random forests, incremental response modeling, scoring, SVM, neural networks, and credit scoring. Strong critical thinking and creative problem‑solving skills. Strong planning and organizational skills. Strong verbal, presentation, and written skills and English fluency. Demonstrated success and effectiveness working in a time‑critical production environment. Familiarity with SQL, Oracle, or other relational database software including manipulation of large data sets. Familiarity with BI, Spotfire, Tableau, or other data visualization software and techniques. Proficiency in MS Office suite (Excel, PowerPoint, Word) and/or Google Office Apps (Sheets, Docs, Slides, Gmail). Knowledge of Atlassian suite of software including Bitbucket, Confluence, Jira, Hipchat, Crucible, and Fisheye. Knowledge of Apache Spark ecosystem, Databricks, and AWS. #J-18808-Ljbffr