About the Role
As a Lead Data Science team member at NielsenIQ, you will play a critical role in developing the next generation of retail measurement solutions. These solutions will be powered by algorithms, ML, and AI, leveraging large volumes of internal, client, and third-party data. You will be actively involved in the entire product creation process, from ideation and Proof Of Concept to experimenting for methodological development, prototyping, productionalization, and “Go to Market” strategy. This role offers the opportunity to explore the newest technologies and applications for data architecture and data modelling. You will collaborate with talented individuals, including data scientists, product managers, and data collection specialists, with the primary goal of accelerating innovation.
Responsibilities
- Work across functions, including with other data scientists specializing in different areas, on various projects, including R&D and automation to solve daily business needs.
- Translate clients’ requirements into actionable solutions or products.
- Ideate and develop solutions for the product innovation pipeline.
- Define business problems as scientific research problems.
- Define and own the roadmap for R&D and prototyping solutions.
- Research solutions, set and execute experiments, prototype solutions, and support the Technology team in productionalizing them.
- Build statistical and analytical models (including ML/AI approaches) as prototypes and POCs to address specific client business needs.
- Test and optimize those models using big data, fine-tune parameters, and iterate.
- Document the solution and experimentation process, and create specifications for the Technology team to productionize it.
- Read, write, comment, maintain, and share legible and quality computer code utilized in prototyping and suctioning.
Requirements
- Education: Bachelor's or Master's degree or higher in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field requiring outstanding analytical expertise and a strong technical background. A PhD level in the area is a plus.
- Experience: 6+ years of experience in data science/research.
- Leadership: Experience in independently leading and owning end-to-end research-related projects.
- Methodologies: Vast knowledge of statistical and machine learning methodologies, including Sampling theory, Probability Theory, Variation analysis, Outlier identification techniques, Regressions, Classification, Time series analysis, Clustering, Monte Carlo simulations, Neural Networks, etc.
- Programming: Proficient in Python and its most common data science libraries.
- Cloud & ETL: Experience with cloud computing and storage software, ETL.
- Teamwork: Ability to lead and influence other functional areas as a team and deliver results on time and per-spec.
- Experimentation: Experience in designing experiments, prototyping, and supporting pilot programs for R&D purposes.
Preferred Qualifications
- Domain experience in managing clients and proven experience of solving complex client requirements.
- Strong communication skills and ability to present and explain methodological and operational solutions to executive leadership.