Business Intelligence & Data Science Associate

The Business Intelligence and Data Science Associate gather requirements, develops, implements, and operationalizes advanced data models for descriptive, diagnostic, predictive and prescriptive analytics using enterprise and operational data to support customer experience and engagement transformation, operational efficiency, revenue generation and protection, cost avoidance and other business metrics. This role has broad and deep knowledge in machine learning, big data processing, and machine learning operations

Responsibilities:

• Work and collaborate with business partners in the requirements gathering and analysis to accurately define required inferences, analytics, and application of data science.

• Initiates analytics models and data solutions to answer business questions and promote data-driven decisions.

• Initiates end-to-end big data pipeline for feature engineering, analytics model training, testing, recalibration, and operationalization.

• Initiates analytical modelling using statistical methods, unsupervised and supervised machine learning algorithms, graphs, and deep learning methods to support customer experience and engagement transformation, operational efficiency, cost optimization, and new revenue creation.

• Maintains an accurate inventory of past insights solutions, processes, models, and techniques.

• Provide subject matter expertise in big data, big data processing, data analytics and data science during consultation discussions with business stakeholders

• Build and construct prototypes and proof of concepts for insights exploration and presents outputs to stakeholders

• Partner with Data Analysts for the design of visualizations and required data models to answer business questions

Required Skills:

• Expertise in Python, PySpark/Spark, SparkR/R scripting, and SQL

• Expertise in data visualization using R and Python or PowerBI

• Expertise in data exploration and, analysis and data storytelling

• Expertise in statistical, machine learning and deep learning techniques for data analytics.

• Expertise in the full data analytics lifecycle including analytics models' operationalization.

• Expertise in building end-to-end big data pipelines with integration to operationalized data models.

• Experience in applications of Large Language Models and AI Agent

• Working knowledge in data operations, quality, governance, and data engineering

• Working knowledge in project management