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TWG Resources LLC is seeking a Director, Data Science in Chicago, IL with the following requirements: Bachelor’s degree in Computer Science, Computer Information Systems, Data Science, Information Systems, Engineering, Applied Mathematics, or related field, or foreign equivalent degree plus 5 years of related experience. Required skills: Design and deploy ML models for financial analytics (3 yrs); Architect cloud-based data pipelines (2 yrs); Develop quantitative models for IRR, MOIC, MVOC, WARF metrics (4 yrs); Implement scalable backend systems using Java and Spring Boot (5 yrs); Build APIs and integrations using Kafka and REST services (4 yrs); Apply LLMs and Generative AI for document (2 yrs); Utilize SQL and data warehousing for financial data processing and analytics (5 yrs); Lead cross-functional AI/ML projects with engineering and business teams ( 5 yrs). 100% telecommuting; can reside anywhere in the US. Co headquarters in Chicago, IL. The expected base pay for this position is $139,818 - $360,000. A discretionary bonus will be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits.
Please note, TWG Resources, LLC is an affiliate of TWG Global, LLC.
Please apply on-line at , scroll down, click Careers and search for job title.
Requirements
Develop and Deploy ML Models: Contribute to the development and deployment of machine learning models for applicant generation and ranking, aimed at enhancing multimodal feed recommendations (e.g., image, text). Engage in applied ML research to iteratively improve recommendation quality and performance.
Integrate ML Innovations: Collaborate with research, search, discovery, shopping, and engineering teams to integrate ML breakthroughs into feed products and services, driving innovation and enhancing user experiences.
Enhance Personalization Features: Implement advanced personalization features and exploration capabilities to drive improvements in key product metrics and overall user engagement.
Design Scalable Solutions: Participate in the design and development of both frontend and backend solutions, ensuring adherence to best practices in technical design, solution development, test automation, and code quality. Maintain high standards in code modularity and sustainability.
Optimize ML Models and Pipelines: Contribute to the development, optimization, and redesign of ML models and data pipelines to enable scalable capabilities for first-party products and services, ensuring robust and efficient ML operations.
Facilitate Cross-Functional Collaboration: Work with cross-functional teams, including Technical Program Managers, Data Engineers, Data Scientists, Business Analysts, and Operations, to ensure seamless integration and execution of AI/ML solutions. Analyze business impacts and drive resolutions for system issues.
Support Cloud Engineering for AI/ML Platforms: Contribute to cloud engineering initiatives to enable robust ML platforms for training and deploying cloud-based AI/ML solutions. Ensure architecture and infrastructure are optimized for scalability, performance, and security.
Develop Generative AI Solutions: Assist in the integration and deployment of generative AI solutions using cloud-based models. Drive innovation in generative AI technologies to create new products and enhance existing offerings.
Deliver Scalable ML Solutions: Deliver scalable solutions for deploying and managing Machine Learning and HPC systems on Google Cloud. Identify and validate valuable deployment configurations to optimize performance and resource utilization.
Design Reference Architectures: Design and maintain reference architectures that leverage a wide range of components, including operating systems, platforms, orchestration systems, frameworks, storage stacks, and consumption models, to support diverse AI/ML workloads.
Advance Large Language Models (LLMs): Contribute to the development and deployment of personalized LLMs to enhance search and recommendation systems. Conduct applied research on emerging LLM techniques to address specific product use cases.
Foster LLM Integration: Collaborate with cross-functional teams to integrate LLMs into products and services, ensuring that innovations in LLM technology are fully leveraged to improve user experience and business outcomes.
Shape Search and Personalization Roadmaps: Execute against the roadmaps for search and discovery personalization efforts. Establish key partnerships and collaborations with internal and external stakeholders to drive innovation and excellence in AI/ML initiatives.
