Design and deliver data-driven solutions that turn complex enterprise data into actionable insights and intelligent products for leading global companies.
Responsibilities
Build and deploy machine learning models and analytical solutions that solve real business problems at scale for enterprise clients.
Design and implement advanced analytics pipelines – from data exploration and feature engineering through model training, evaluation, and production deployment.
Leverage GenAI and LLMs to augment data science workflows: conversational analytics, Text-to-SQL, automated insight generation, and intelligent data exploration over enterprise datasets.
Translate business requirements into data science approaches – selecting the right methods, validating assumptions, and communicating results to both technical and non-technical stakeholders.
Collaborate with engineers, architects, and consultants on cross-functional project teams, contributing to solution design and technical direction.
Requirements
Bachelor’s degree or higher in Computer Science, Mathematics, Statistics, Data Science or another related technical field
Design, build, and maintain data platforms and products that support credit decisioning, risk analytics, regulatory reporting, and machine learning initiatives.
Compensation
O'Fallon, Missouri: $92,000 - $147,000 USD
Responsibilities
Design, develop, test, and maintain scalable data pipelines, transformations, and datasets supporting ECR products and initiatives.
Build and optimize ETL/ELT workflows using technologies such as SQL, Python, Databricks, and Spark.
Develop and maintain curated data products that support reporting, analytics, credit decisioning, and other business use cases.
Partner with Product, Risk Analytics, Data Science, and Engineering teams to understand requirements and translate them into effective technical solutions.
Implement data validation, testing, and quality controls to ensure data accuracy, consistency, and reliability.
Requirements
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related STEM discipline
Lead the Computer Science program with a focus on data engineering and analytics, developing curriculum, fostering industry partnerships, and guiding faculty and students.
Responsibilities
Provides leadership for the Computer Science program within the Department of Natural, Health, and Mathematical Sciences
Leads the design, development, and launch of undergraduate and/or graduate curriculum in data engineering and data analytics
Integrates industry-standard tools and practices (e.g., SQL, Python, cloud data services, distributed data processing, and business intelligence platforms) into coursework and lab experiences
Oversees program assessment, including collecting, analyzing, and reporting Computer Science program learning outcomes
Recruits, supports, and oversees computer science adjunct faculty to ensure quality and consistency of instruction
Software Engineer focused on building and scaling data infrastructure for next-generation robotics and Physical AI, focusing on high-volume data pipelines and scalable production systems.
Responsibilities
Design, build, and maintain scalable data processing and delivery pipelines.
Develop infrastructure for large-scale data transformation, packaging, and storage.
Work with various data formats, schemas, metadata, and custom client requirements.
Build reliable data delivery pipelines using AWS/GCP and distributed processing technologies.
Collaborate directly with customer engineering teams to develop customized solutions.
Lead the transformation of the Performance Attribution Platform by building scalable, cloud-native, accelerating AI adoption, and enabling data-driven investment decisions across all asset classes.
Responsibilities
Lead the transformation of the Performance Attribution Platform by building scalable, cloud-native, accelerating AI adoption, and enabling data-driven investment decisions across all asset classes.
Define and execute the roadmap for a unified performance attribution platform supporting all asset classes.
Drive platform simplification, modernization, and cloud-native transformation.
Partner with Investments, Client Reporting, Data, and AI leaders to deliver strategic business outcomes.
Enable AI-powered performance attribution workflows that improve productivity, insight generation, and investment outcomes.
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