The Role / Responsibilities:
The Senior Research Scientist, Artificial Intelligence/Machine Learning is a core member of the Emerging Business Unit (EBU) team, reporting to the Senior Director, ML and AI, Emerging Business Unit. This is a newly formed, highly visible team that is key to long-term growth strategy that will lead our efforts to better understand and adapt to an environment characterized by widespread, technology-driven change. The EBU is charged with supporting innovation within our existing LOBs, developing opportunities in the "whitespace", enhancing our innovation process and understanding customers technology preferences.
As such, the Senior Research Scientist, Artificial Intelligence/Machine Learning will, independently and in collaboration with the Senior Director, research, design, develop, and implement innovative Machine Learning, AI, deep learning, NLP, Data Science solutions that will advance the organization's capabilities across multiple business lines. On any day, the candidate could be doing any or all of the following:
-Research emerging ML/AI/NLP/Data Science solutions and be conversant with latest developments in these fields
-Brainstorm with internal stakeholders, and external clients to identify innovations in ML/AI/NLP to help advance automation, knowledge discovery, decision-making and insights, and streamline business processes or enable new capabilities,
-Implement and prototype new algorithms and write code for novel ML/AI/NLP solutions,
-Evaluate custom solutions through prototyping, POCs and quantitative metrics, and handing off solutions to stakeholder teams as needed -Discuss, brainstorm new advanced technology solutions with team members
-Explain complex models to non-experts, in layperson terminology to clients, stakeholders and managers, while also being able to discuss intricacies of complex algorithms with experts in the field
-Determine tradeoffs between internal technical implementation vs. partnerships with external teams/organizations/vendors for new technology-based solutions and capabilities
-Prepare reports, presentations, for internal and external stakeholders, and as applicable, publish in peer-reviewed journals and magazines.
-Attend, present at technical conferences, workshops, and meetups
- Advanced or basic degree (PhD with few years' experience, or MS / BS (with many years' experience)) in a quantitative field such as CS, EE, Information sciences, Statistics, Mathematics, Economics, Operations Research, or related, with focus on applied and foundational Machine Learning , AI , NLP and/or / data-driven statistical analysis & modelling
- Experience in applying AI/ML/ NLP / deep learning / data-driven statistical analysis & modelling solutions to multiple domains, including financial engineering, financial processes a plus.
- Strong Knowledge of the theory and applications of machine learning, AI, deep learning, data science, NLP, text analytics, unstructured data analytics, supervised/unsupervised learning. Experience with image and video processing is a plus.
- Strong, proven programming skills in Python, C/C++, Java, R , MATLAB, Scala, and with machine learning and deep learning and Big data frameworks including TensorFlow, Caffe, Spark, Hadoop. Experience with writing complex programs and implementing custom algorithms in these and other environments.
- Experience beyond using open source tools as-is, and writing custom code on top of, or in addition to, existing open source frameworks.
- Proven capability in demonstrating successful advanced technology solutions (either prototypes , POCs, well-cited research publications, and/or products) using ML/AI/NLP/data science in one or more domains,
- Experience in data management, data analytics middleware, platforms and infrastructure, cloud and fog computing is a plus
- Experience in data visualization solutions and data visualization tools is a plus
- Additional experience with GPU programming for training deep learning models, and cloud environments such as AWS, Azure is desirable
- Excellent communication skills (oral and written) to explain complex algorithms, solutions to stakeholders across multiple disciplines, and ability to work in a diverse team
- Experience in an applied R&D environment , working in an agile, innovation-lab culture to bring cutting-edge technologies to fruition, from initial concept to implementation