Data Scientist - Customer & Loyalty

Circle K; Gurugram District; Sur place; INR 1 200 000 - 1 800 000; Plein temps

Résumé du poste

A global convenience store leader is seeking a Data Scientist in Gurugram to collaborate with various teams and deliver advanced analytics projects that drive business results. The ideal candidate should possess a strong background in data science, statistical modeling, and big data technologies, with a bachelor's degree in a quantitative field. This role involves working onsite full-time in a dynamic and collaborative environment.

Qualifications

• 3-4 years in a data science or advanced analytics role.
• Functional Analytics knowledge in Supply chain and Marketing.
• Proficiency in statistical modeling and big data technologies.

Responsabilités

• Analyze large-scale structured and unstructured data.
• Develop deep-dive analyses and machine learning models.
• Communicate complex models effectively to non-technical stakeholders.

Connaissances

Delivery Excellence

Business Acumen

Social Intelligence

Innovation and Agility

Formation

Bachelor’s degree in a quantitative field

Master’s degree preferred (MBA, MS, M.Tech)

Outils

R

Python

KNIME

AWS

Power BI

Tableau

Description du poste

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Job Description

Alimentation Couche-Tard Inc. (ACT) is a global Fortune 200 company and a leader in the convenience store and fuel space, with a footprint across 31 countries and territories. The Circle K India Data & Analytics team is an integral part of ACT’s Global Data & Analytics Team. The Data Scientist/Senior Data Scientist will be a key player in this team, helping to grow analytics globally at ACT.

The selected candidate will collaborate with multiple departments, including Global Marketing, Merchandising, Global Technology, and Business Units.

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Department: Data & Analytics

Location: Cyber Hub, Gurugram, Haryana (5 days in office)

Job Type: Permanent, Full-Time (40 Hours)

Reports To: Senior Manager Data Science & Analytics

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About The Role

The incumbent will be responsible for delivering advanced analytics projects that drive business results, including interpreting business needs, selecting appropriate methodologies, data cleaning, exploratory data analysis, model building, and creating polished deliverables.

Roles & Responsibilities

• Analyze large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas.
• 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, providing detailed descriptions to promote data-driven decisions.
• Use cloud setups to extract processed data for modeling and analysis, and visualization tools to represent large datasets.

Operational Excellence

• Follow industry standards in coding solutions and software development lifecycle to ensure best practices.
• Formulate hypotheses, develop analyses, build data models, and clarify complex problems.
• Partner with Data Engineering to build and maintain data infrastructure, pipelines, and workflows for automation.

Stakeholder Engagement

• Collaborate across teams—business, data engineers, visualization experts—to deliver projects.
• Communicate complex models and insights clearly and effectively to non-technical stakeholders.

Job Requirements

Education

• Bachelor’s degree in a quantitative field (Statistics, Business Analytics, Data Science, Math, Economics, etc.).
• Master’s degree preferred (MBA, MS, M.Tech in Computer Science, etc.).

Relevant Experience

• 3-4 years in a data science or advanced analytics role.

Behavioral Skills

• Delivery Excellence
• Business Acumen
• Social Intelligence
• Innovation and Agility

Knowledge

• Functional Analytics (Supply chain, Marketing, Customer Analytics)
• Statistical modeling with tools like R, Python, KNIME
• Statistics and experimental design (A/B testing, hypothesis testing, causal inference)
• Building scalable ML models, feature engineering, evaluation metrics, statistical inference
• Deploying models with MLOps tools (MLflow, DVC, Docker)
• Proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow)
• Big data technologies (AWS, Azure, GCP, Hadoop, Spark)
• Database management (MySQL, SQL Server, MongoDB, DynamoDB)
• Business intelligence tools (Power BI, Tableau, Alteryx)
• Microsoft Office applications

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Engineering and Information Technology

Industries

• Retail

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