Data Modeler

A leading enterprise organization is seeking a Senior AI/ML Engineer to support the development of advanced predictive analytics and machine learning solutions within a large-scale data environment. This role will focus on designing, building, and refining models that identify patterns, detect anomalies, and generate actionable insights from complex datasets. The ideal candidate combines strong machine learning expertise, software engineering fundamentals, and experience working with distributed data platforms to solve challenging analytical problems at scale. What You'll Tackle: • Design, develop, and refine predictive models that support advanced analytics and risk detection initiatives. • Analyze structured and unstructured datasets to identify meaningful patterns, relationships, and behavioral signals. • Develop feature engineering strategies and model inputs to improve predictive performance. • Build and test machine learning workflows using Python, PySpark, and distributed computing technologies. • Collaborate with engineering, data, and analytics teams to support scalable model development and deployment. • Support AI/ML lifecycle activities including model training, validation, monitoring, and optimization. • Contribute to the development of behavioral analytics, anomaly detection, and advanced modeling techniques. • Work within modern cloud and distributed processing environments to support large-scale analytics. • Document methodologies, assumptions, and model outcomes for technical and business stakeholders. • Support continuous improvement of AI/ML processes, tooling, and operational practices. What You Bring: • Strong computer science, software engineering, mathematics, statistics, or related technical background. • Experience developing predictive models, machine learning solutions, or advanced analytics applications. • Strong proficiency in Python. • Working knowledge of Java is a plus. • Hands-on experience with Databricks and PySpark. • Experience with distributed data processing and scalable analytics environments. • Background in AI/ML, data science, predictive analytics, and statistical modeling. • Ability to work with complex datasets and ambiguous problem spaces. • Strong problem-solving, analytical, and critical-thinking skills. • Ability to clearly explain technical concepts, modeling approaches, and results. Nice to Have: • Experience with anomaly detection, behavioral analytics, fraud analytics, or risk analytics. • Familiarity with graph analytics, interaction-based modeling, or complex systems analysis. • Experience working with enterprise-scale data platforms. • Experience operationalizing and monitoring machine learning models in production environments. • Exposure to cloud-based analytics ecosystems and modern data architectures. What Success Looks Like: • Delivery of scalable, high-performing machine learning solutions. • Development of actionable insights from complex data environments. • Strong collaboration across data, engineering, and business teams. • Continuous improvement of model accuracy, performance, and operational efficiency. • Successful deployment and adoption of AI/ML capabilities within production environments. • Contribution to innovative analytics and data-driven decision making.