Data Scientist

CONTEXT

Develop GenAI/AI/ML models to drive insights and support data-driven decision-making across various departments. Work collaboratively with cross-functional teams to analyze complex datasets and deliver actionable recommendations that enhance business strategies, drive user acquisition and retention, improve customer experience and usage stimulation for B2C, B2B and Bank user base.

SCOPE & RESPONSIBILITY

1. Develop analytics LLM, ML/AI models for Boost use-cases (B2C, B2B and Bank), such as agentic AI, Chatbot, recommendation engine, predictive model, life-time value model and segmentation.
2. Support Generative AI development and deployment projects by developing ML/AI models to complement various LLM for ChatGPT.
3. Develop, validate, and deploy predictive models that enhance decision-making processes across various business units.
4. Train, test, validate and deploy agentic AI & LLM Chatbot that improve work efficiency and enhance customer experience across various business units.
5. Stay abreast of industry trends, emerging technologies, and best practices in data science and analytics.
6. Conduct comprehensive data analyses using advanced statistical methods and machine learning algorithms to derive insights from structured and unstructured data.
7. Collaborate with cross-functional teams to identify key business challenges and formulate data-driven strategies to address them.
8. Design and implement robust data pipelines and workflows to facilitate the collection and processing of large-scale datasets.
9. Create data visualizations and reports to communicate findings and recommendations to technical and non-technical audiences.
10. Perform A/B testing and other experimental methodologies to assess the effectiveness of business initiatives.

KEY REQUIREMENTS

1. Bachelor or master’s degree in data science, Statistics, Mathematics, Computer Science, or a related quantitative field.
2. Proficiency in programming languages such as Python or R, JSON along with strong SQL skills for data manipulation and querying.
3. A minimum of 4 years of experience of data analysis, statistical modeling, machine learning, GenAI and Agentic AI applications.
4. Extensive experience with data visualization tools (e.g., QuickSight or similar) for presenting analytical results.
5. Knowledge in Telecommunications or Digital banking/Fintech and payment sector is preferred.
6. Passionate about creating value through scientific methods and data analysis.
7. Fluent in verbal and written communication with the ability to articulate data science/analytics in simple terms.
8. Familiar with high dimensionality data, dimensionality reduction, or feature extraction.
9. Able to explore, manipulate, and visualize data in big data to find new patterns and signals.
10. Proficient in statistical analysis, quantitative analytics, predictive analytics, multivariate testing, and optimization algorithms