Junior Data Quality Scientist at the Coca-Cola Company

Junior Data Quality Scientist at the Coca-Cola Company

Category: Finance

Team: Data, Insights & Analytics

Job Goal

The Data, Insights & Analytics (DIA) team’s DQ Scientist with Anomaly Detection (Data Error) and Remediation focus will implement and execute business-led data governance initiatives to maintain the upstream data value chain’s quality using intelligent solutions and an End-to-End view.
“Know the data on both sizes (business and technical)”: understand the business requirements of data quality and translate them to intelligent solutions; “Do the hands-on”: implement data quality detection and remediation through automation and intelligent; and “Live with innovation”: understand the iterative nature of data projects with strong innovation mindsets to think ?outside the boxes? then try/learn/improve.
Collaboration with data curators, engineers, scientists, functional teams, data owners/stewards, and other DIA Data & Analytics leads is required for this effort.
New Key Duties
This crucial role requires you to collaborate with our functional data owners and data stewards to ensure data quality for all our data users.
Analysis/Translation:

With Explorative Data Analytics skills, understand use case data purposes and usage. The meaning of data goes beyond statistics. So read and discuss data like your favourite literature.
Transfer company ideas to reality and implementation with strong Business and technical data expertise Data is like languages?business talks about it in the business manner, tech talks about it in the technical way?you must be multilingual.
Try new innovative methods to make DQ more intelligent and automated. Unexpected data errors are avoidable, but no one wants to live there. Can you discover them in real time without an army of analysts and engineers?
Join a multidisciplinary team (Data Scientist, Insights Expert, Data Engineer, Data Curator, Vendors) with strong problem-solving skills like active listening, research, creativity, communication, etc. Our data landscape is huge with various kinds of data responsibilities as a large organisation. For “Go-big” thinking, this is “Home”
Delivered manually:

Use appropriate data science methods (unsupervised, semi-supervised, supervised, and/or smart rule engines) to solve data challenges, support ideation, and early-stage PoC.
Lead scaled innovation and enhancement of our data quality tools and solutions (Can you move the successful PoC to big?
Set an example for data best practises and explore ways to improve. (Nobody lives in a perfect world, but do you dare to challenge yourself and others to make things better? We’re designing solutions to verify others’ work, therefore ours must be “sky high” in quality and intellect.
Requirements
Your secret ingredients?

Prefered: Master’s Degree in Computer Science, Engineering, or Data Science with 1-2 years of data science experience necessary.
Experienced in converting business difficulties into value-add and technically capable end solutions
Strong data management skills
The Data Science Semi-supervised, supervised, and unsupervised learning (notably outlier detection)
Clustering, Dimension reduction, Word vectors/Embeddings, Classification, Regression, Isolation Forest
Python, SQL, PySpark ETL
Data platforms: SQL DB, Datalake, Jupyer, Databricks
Share your Github or other code repository link if possible.
Learn agile product delivery (SAFE is best).
CPG and core dataset experience is a plus.