Description :

Role Summary :

As a data scientist, you will be part of the analytics team that develops data-driven insights across cross-functional teams in all countries to drive efficiency and support strategic business decisions. You will research, design, and implement cutting-edge algorithms to analyse diverse sources of data to achieve the target outcomes

Roles & Responsibilities :

- End-to-End Project Development : Lead the conceptualization, development, and execution of data science projects across various functions and geographies, 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.

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.

Key Skills :

- Technical Proficiency : Proficient in programming languages such as Python and 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 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.

Skills Required :

- 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.