Develop GenAI/AI/ML models providing descriptive, predictive, or prescriptive insights into business-critical initiatives such as acquisition, engagement, and retention of the B2C, B2B and Bank user base
Responsibilities
- Develop analytics ML/AI models for Boost use-cases (B2C, B2B and Bank), such as recommendation engine, predictive churn, life-time value and segmentation.
- Support Generative AI deployment projects by developing ML/AI models to complement various LLM.
- Perform data wrangling and extract exploratory insights from big data. Create statistical and machine learning models in multiple technologies to support project goals. Then, deliver solutions to loosely defined business problems by leveraging pattern detection over potentially large datasets
- Conduct advanced data analysis and complex design algorithms, identifying available and relevant data, both internal and external data sources, leveraging on new data collection processes.
- Solve analytical problems, articulate the findings and methodologies used, explore various approach to validate business findings and hypothesis.
- Utilize business acumen coupled with strong analytical and problem-solving skills to decide on the optimum programming options to produce value-add solutions. Solutions must be focused to achieve desired product activation, GTV, NR customer satisfaction, and cost efficiency.
- Work closely to provide cross-functional solutions for the various business units to develop various descriptive, predictive or prescriptive models
Requirements:
- Bachelor of Science degree in computing/programming/machine learning or other related fields.
- Strong programming and statistical modeling skills with tools such as Python, R, SQL, SAS, Weka, MATLAB etc. (with Python and Linux command line skills).
- A minimum of 4 years of experience of relevant quantitative and qualitative research and analytics
- Experience in coding using machine learning algorithms.
- Knowledge in Telecommunications or Digital banking/Fintech and payment sector is preferred.
- Passionate about creating value through scientific methods and data analysis.
- Fluent in verbal and written communication with the ability to articulate data science/analytics in simple terms
- Familiar with high dimensionality data, dimensionality reduction, or feature extraction.
- Able to explore, manipulate, and visualize data in big data to find new patterns and signals.
- Proficient in statistical analysis, quantitative analytics, predictive analytics, multivariate testing, and optimization algorithms
