Data Scientist

Job Title: Data Scientist Job Type: Permanent, Full-time Function: Information Technology Business: Godrej Consumer Products Limited Location: Mumbai, Maharashtra, India About Godrej Industries Limited and Associate Companies (GILAC) GILAC is a holding company of the Godrej Group. We have significant interests in consumer goods, real estate, agriculture, chemicals, and financial services through our subsidiary and associate companies, across 18 countries. https://www.godrejindustries.com/ About Godrej Consumer Products Limited (GCPL) Godrej Consumer Products is a leading emerging markets company. As part of the over 125-year young Godrej Group, we are fortunate to have a proud legacy built on the strong values of trust, integrity and respect for others. At the same time, we are growing fast and have exciting, ambitious aspirations. https://www.godrejcp.com/ About the role As Data and Analytics manager, you will be responsible for managing the design, development, and implementation of Azure data engineering/visualization and data science solutions in GCPL with a specific focus on Media and Marketing. This role requires a blend of hands-on technical expertise, and strong business understanding. The responsibility also includes evaluation and implementation of emerging technologies in Azure data tech stack, data science, AI/ML and drive standardization and implementation of best practices. Your Roles & 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. Position Requirements Qualification & Experience: 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. Skills Required: Must have: Technical Proficiency: 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. 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. Project Lead : Experience in leading analytics projects and initiatives, Ability to oversee analytics roadmaps and cross‑functional project execution 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. Good to Have: Deep Learning Frameworks: Exposure to deep learning frameworks such as TensorFlow, PyTorch, or Keras, with application in areas like time-series forecasting. 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. 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. AI Productization: Experience in translating data science models into bus