Senior Data Scientist
Support Office India
Circle K
Circle K is a convenience store and gas station chain offering a wide variety of products for people on the go. Visit us today!Job Description
Alimentation Couche-Tard Inc., (ACT) is a global Fortune 200 company. A leader in the convenience store and fuel space with over 16,700 stores in 31 countries, serving more than 9 million customers each day. The India Data & Analytics Global Capability Centre is an integral part of ACT’s Global Data & Analytics Team and the Senior Data Scientist will be a key player on this team that will help grow analytics globally at ACT.
The hired candidate will partner with multiple departments, including Global Marketing, Merchandising, Global Technology, and Business Units.
About the Role
The incumbent will be responsible for delivering advanced analytics projects that drive business results including interpreting business, selecting the appropriate methodology, data cleaning, exploratory data analysis, model building, and creation of polished deliverables.
Responsibilities
Analytics & Strategy
Analyse large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas of the business
Utilize data mining, statistical and machine learning techniques to derive business value from store, product, operations, financial, and customer transactional data
Apply multiple algorithms or architectures and recommend the best model with in-depth description to evangelize data-driven business decisions
Utilize cloud setup to extract processed data for statistical modelling and big data analysis, and visualization tools to represent large sets of time series/cross-sectional data
Operational Excellence
Follow industry standards in coding solutions and follow programming life cycle to ensure standard practices across the project
Structure hypothesis, build thoughtful analyses, develop underlying data models and bring clarity to previously undefined problems
Partner with Data Engineering to build, design and maintain core data infrastructure, pipelines and data workflows to automate dashboards and analyses
Stakeholder Engagement
Working collaboratively across multiple sets of stakeholders – Business functions, Data Engineers, Data Visualization experts to deliver on project deliverables
Articulate complex data science models to business teams and present the insights in easily understandable and innovative formats
Job Requirements
Education
Bachelor’s degree required, preferably with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.)
Master’s degree preferred (MBA/MS Computer Science/M.Tech Computer Science, etc.)
Relevant Experience
5–7 years of relevant working experience in a data science/advanced analytics role
Behavioural Skills
Delivery Excellence
Business disposition
Social intelligence
Innovation and agility
Knowledge
Functional Analytics (Supply chain analytics, Marketing Analytics, Customer Analytics)
Statistical modelling using Analytical tools (R, Python, KNIME, etc.) and use big data technologies
Knowledge of statistics and experimental design (A/B testing, hypothesis testing, causal inference)
Practical experience building scalable ML models, feature engineering, model evaluation metrics, and statistical inference.
Practical experience deploying models using MLOps tools and practices (e.g., MLflow, DVC, Docker, etc.)
Strong coding proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow, etc.)
Big data technologies & framework (AWS, Azure, GCP, Hadoop, Spark, etc.)
Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational (MongoDB, DynamoDB) database management systems and Data Engineering tools
Business intelligence & reporting (Power BI, Tableau, Alteryx, etc.)
Microsoft Office applications (MS Excel, etc.)
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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: A/B testing Architecture AWS Azure Big Data Business Analytics Business Intelligence Causal inference Computer Science Data analysis Data Mining Data visualization Docker DynamoDB Economics EDA Engineering Excel Feature engineering GCP Hadoop KNIME Machine Learning Mathematics MLFlow ML models MLOps MongoDB MySQL Pandas Pipelines Power BI Python PyTorch R Scikit-learn Spark SQL Statistics Tableau TensorFlow Testing Unstructured data
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