Job
Description
Who you areA senior Data Scientist specializing in Advanced Analytics, with expertise in machine learning (ML), predictive modeling, and statistical analysis. Sound experience in leveraging Big-data technologies, AI, and automation to solve complex business problems and enhance decision-making. Have experience working with Cloudera Data Platform, Apache Spark, Kafka, and Iceberg tables, and you understand how to design and deploy scalable AI/ML models. Your role will be instrumental in data modernization efforts, applying AI-driven insights to enhance efficiency, optimize operations, and mitigate risks.What you’ll doAs a Data Scientist – Advanced Analytics, your responsibilities include: AI & Machine Learning Model Development Developing AI/ML models for predictive analytics, fraud detection, and customer segmentation. Implementing time-series forecasting, anomaly detection, and optimization models. Working with deep learning (DL) and Natural Language Processing (NLP) for AI-driven automation. Big Data & Scalable AI Pipelines Processing and analyzing large datasets using Apache Spark, PySpark, and Iceberg tables. Deploying real-time models and streaming analytics with Kafka. Supporting AI model training and deployment on Cloudera Machine Learning (CML). Advanced Analytics & Business Impact Performing exploratory data analysis (EDA) and statistical modelling. Delivering AI-driven insights to improve business decision-making. Supporting data quality and governance initiatives using Talend DQ. Data Governance & Security Ensuring AI models comply with Bank’s data governance and security policies. Implementing AI-driven anomaly detection and metadata management. Utilizing Thales CipherTrust for data encryption and compliance. Collaboration & Thought Leadership Working closely with data engineers, analysts, and business teams to integrate AI-driven solutions. Presenting AI insights and recommendations to stakeholders and leadership teams. Contributing to the development of best practices for AI and analytics. Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise 3-7 years of experience in AI, ML, and Advanced Analytics. Proficiency in Python, R, SQL, and ML frameworks (Scikit-learn, TensorFlow, PyTorch). Hands-on experience with Big-data technologies (Cloudera, Apache Spark, Kafka, Iceberg table format). Strong knowledge of statistical modelling, optimization, and feature engineering. Understanding of MLOps practices and AI model deployment. Preferred technical and professional experience Develop and implement advanced analytics models, including predictive, prescriptive, and diagnostic analytics to solve business challenges and optimize decision-making processes. Utilize tools and technologies to work with Large and complex datasets to derive analytical solutions. Build and deploy machine learning models (supervised and unsupervised), statistical models, and data-driven algorithms for forecasting, segmentation, classification, and anomaly detection. Should have strong hands-on experience in Python, Spark and cloud computing. Should be independently working and be able to deploy deep learning models using various architectures. Should be able to perform exploratory data analysis (EDA) to uncover trends, relationships, and outliers in large, complex datasets. Design and create features that improve model accuracy and business relevance. Should create insightful visualizations and dashboards that communicate findings to stakeholders. Effectively translate complex data insights into clear and actionable recommendations. Work closely with business leaders, engineers, and analysts to understand business requirements and translate them into analytical solutions that address strategic goals. Exposure to Graph AI using DGraph Enterprise. Knowledge of cloud-based AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI).
