Senior Analyst - Data Science

Key Skills

Aartificial intelligence, Analytical and Problem solving, Apache Hadoop, Apache Hive, Apache Pig, API, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data Analysis, Data science techniques, DevOps, Effective communication skills, ETL frameworks, Github, Kubeflow, Machine learning techniques, MLflow, MLOps tools, Python Programming, R Programming, Semantic Technologies, SPARK Programming, SQL, Statistical modeling

Job Description

Overview

As the lead data scientist, this individual will be responsible for development and deployment of data science modeling frameworks using Statistical modeling (Machine Learning as appropriate) for sDNA and cDNA for EU markets e.g., prediction of store potential, store segmentation, assortment optimization, asset deployment, audience profiling, etc.

The position exists to unlock competitive advantage in go-to-market and execution through cutting edge data science, advanced analytics, and AI techniques, with a focus on OT, TT and AFH. The focus of this role is ensuring the successful implementation of data science solutions that deliver. The role holder will also leverage best practice to help to establish a reusable data science ecosystem.

This is going to be a technical role however needs to have good functional (CPG/Retail) inclinational for better solution design and ultimately developing solutions those are intuitive for end business users

In Summary this role

• Focused on support for data science and advanced analytics initiatives (sDNA and cDNA)
• E.g., prediction of store potential (MVS), store segmentation (SLC), assortment optimization, asset deployment, audience profiling, etc.., through application of data science and advanced analytics
• Leverage best practice capabilities with GBS and learnings on Data Science applications
• Key responsibilities will be to understand the data availability across EU markets, develop solutions using advanced analytics, artificial intelligence, machine learning and deep learning projects

Responsibilities

Data Analysis and Modeling: Analyzing complex datasets to extract valuable insights and identify trends/patterns. Develop and implement predictive and prescriptive models to support business decision-making. Ensure data quality and integrity throughout the analysis process.

• Working with huge and varied datasets to find sources of strategic advantage in go-to-market and execution, with a focus on Organised Trade, Traditional Trade and Away-From-Home
• Clean and prepare data for use in advanced analytics, artificial intelligence, and machine learning projects
• Build analysis in Python with accompanying documentation
• Designing and using algorithms and building predictive & prescriptive models needed to automate execution and the generation of insights, to enable efficiencies that allow colleagues to focus on value added tasks.
• Provide responses to ad-hoc follow-ups when double-click (additional questions) required with tables/charts for relevant data
• Developing our internal capability to derive insights from structured data, unstructured data through location intelligence, machine learning, collaborative filtering, etc. to unlock new sources of insight that can be used to drive competitive advantage.

Continuous Improvement: Stay up-to-date with emerging trends, technologies, and best practices in data science, machine learning, and AI. Identify opportunities to enhance data science methodologies, tools, and processes within the team.

Qualifications

• Bachelor's or advanced degree in a quantitative field for instance a Master’s degree or PhD in data science or math (e.g., Computer Science, Mathematics, Statistics, Data Science) or equivalent experience
• 7 years + of relevant advanced analytics experience in Marketing or Commercial in either Retail, or CPG industries. Other B2C domains can be considered
• Proven experience with medium to large size data science projects
• Advanced knowledge of key data science techniques:
• Combining data from multiple sources through APIs, Semantic Web, etc.
• Data preparation and feature engineering
• Supervised / Unsupervised learning
• Collaborative Filtering
• Location Analytics & Intelligence
• Proficiency in programming languages such as Python, R, or SQL.
• Good to have experience with data processing frameworks (e.g., Hadoop, Spark)
• Good to have understanding of data engineering principles, data integration, and ETL processes.
• Excellent problem-solving skills and the ability to translate business needs into data-driven solutions.