Manager - Analytics

Job Summary

Our Analytics Team. As a data scientist, you will be a part of the analytics team that develops data driven insights across cross functional teams across all GCPLcountries to drive efficiency and make strategic business decisions. You will research, design and implement cutting edge algorithms to analyse diverse sources of data to achieve the target outcomes. About the role.

Responsibilities

• End-to-End Project Development: Lead the conceptualization, development, and execution of data science projects across various functions and geographies within GCPL, ensuring alignment with business objectives and strategies.
• Cross-functional Collaboration: Foster effective collaboration with internal stakeholders such as marketing, sales, supply chain, and finance to identify data-driven opportunities, address business challenges, and deliver actionable insights.
• Vendor Management: Engage with external vendors and partners to leverage specialized expertise, tools, and resources for advanced analytics projects, ensuring quality deliverables within established timelines and budgets.
• Performance Monitoring: Establish metrics and KPIs to assess the performance and impact of data science initiatives, tracking progress against goals and recommending adjustments as necessary to optimize outcomes.
• Continuous Improvement: Stay abreast of industry trends, emerging technologies, and best practices in data science, actively seeking opportunities to enhance the company's analytical capabilities and drive innovation.
• Team Management: Mentor junior data scientists, providing guidance on project execution, technical skills development, and career growth.

Education

• Educational Background: Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Economics, or related disciplines.

Professional Experience

• Minimum of 5-8 years of experience in data science, preferably within the FMCG industry or related sectors.

Work Experience

• 4-6 years.

Technical Proficiency & Requirements

• Proficient in programming languages such as Python, R, or SQL, with hands-on experience in statistical analysis, machine learning, data visualization, and predictive modeling techniques.
• Cloud for Machine Learning: Experience working with cloud platforms (AWS, GCP, or Azure), especially with ML services like SageMaker, Databricks or Azure ML Studio for scalable and production-grade solutions.
• MLops Understanding: Familiarity with MLops (Machine Learning Operations) principles and practices, including model deployment, monitoring, versioning, and automation, to ensure scalability, reliability, and performance of machine learning models in production environments.
• Proven ownership of end-to-end delivery in cross-functional environments (platform + BT/IT + business), with examples of resolving blockers.
• Strong model review and validation capability (evaluation metrics, robustness, leakage, business sign-off).
• Analytical Skills: Strong analytical and problem-solving skills, with the ability to interpret complex data sets, extract actionable insights, and translate findings into business recommendations.
• Communication Skills: Excellent verbal and written communication skills, with the ability to effectively convey technical concepts to non-technical stakeholders and influence decision-making at all levels of the organization.
• Business Acumen: Sound understanding of FMCG business dynamics, consumer behavior, market trends, and competitive landscape, coupled with a strategic mindset and commercial awareness.
• Adaptability: Proven ability to thrive in a fast-paced and dynamic environment, managing multiple priorities and stakeholders while maintaining a focus on delivering high-quality results.
• Deep Learning Frameworks: Exposure to deep learning frameworks such as TensorFlow, PyTorch, or Keras, with application in areas like time-series forecasting.
• AI Productization: Experience in translating data science models into business products or dashboards embedded within operational processes.
• Generative AI & LLM Integration: Experience architecting and scaling LLM-driven workflows for both unstructured and structured data e.g., embedding large text corpora, generative SQL query development, deploying RAG pipelines using frameworks like Hugging Face Transformers, LangChain, or similar.
• Agentic AI & Autonomous Agents: Familiarity with designing, building, and deploying agentic AI frameworks (e.g., LangChain Agents, Agency OS, AutoGPT) to automate complex, multi-step business processes and enable self-driving data workflows.

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