About the Role

Applied AI Research Scientist is a business-technology-domain focused result-oriented role aimed at bringing the cutting edge of AI research to the forefront of business by building tangible proof-of-concepts, products, utilities, automated processes and playbooks.

Primary role of the incumbent in the Applied AI Research team is to be a highly technical player, with ability to research and come up with impactful ideas across AI/ML, NLP, Knowledge graph (and other statistical, quantitative and engineering techniques) and build scalable and efficient solutions and/or implementation playbook for business application.

As an Applied AI Research Scientist, the incumbent needs to be comfortable operating in a fast paced, results driven environment with water tight deadlines and producing measurable and tangible outcome. S(he) should be able to independently lead projects and work with business stakeholders, data engineering and technology teams to bring solutions to fruition. S(he) should be able to guide others working alongside in same or similar research projects and document findings and learnings in a scientific way.

As an Applied AI Research Scientist, s(he) will also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

Responsibilities:

• He/she would research and create proof of concept products, solutions, playbooks, etc. on topics including (and not limited to) NLP, NLG, Deep Learning, Reinforcement Learning, GAN’s, Active Learning, Knowledge Graph on various application areas like banking, data governance, information security, operations, IT, climate change, etc.
• Evangelize and advocate about the developed AI POC’s, products and solutions across business and broader team – drive adoption
• Partner with universities and co-author papers and publication
• Required to work individually or as part of a team on data science and/or engineering problems and work closely with business partners across the organization

Market Skills and Certifications

Essential

Qualifications

:

• Masters/ PHD in machine learning, information science, engineering, statistics or a highly quantitative field with industry work experience
• Proven ability to relate to and solve business problems through machine learning and statistics
• 2+ years (with PhD) or 4+ years (with Masters) of hands-on experience developing and implementing machine learning algorithms and/or statistical models or with engineering fields
• Strong collaboration skills – work effectively in a matrixed environment — should be able to find ways to identify and secure resources if required
• Experience in developing models and solutions using both standard and advanced AI/ML techniques like Deep Learning, Reinforcement Learning, GAN’s, graphical models, etc.
• Deep knowledge in areas such as Knowledge Graph modeling, AI/ML driven search applications, document Q&A systems, Chatbots, fairness/bias in AI models, Causal inference, etc.
• Experience in developing NLP/NLG models across fields of topic modeling, text mining, documents, speech, etc. Experience in advanced concepts like Transformers is a bonus
• Strong programing skills using Python (with Anaconda), Scala, Java, Shell scripting, etc.
• Proven experience in leveraging open source AI/ML packages (including names such as Tensorflow, Keras, Pytorch, NLTK, TextBlob, SpaCy, AllenNLP, MonkeyLearn, Hugging face, BERT, GPT-3, etc.)
• Experience with third-party/vendor AI/ML capabilities like Neo4J, IBM Watson, Snorkel, Lucid, etc.
• Experience in one or more of Big Data skills – SQL, Aster, Teradata, Hadoop, SPARK

Desired

Qualifications:

• Knowledge of banking industry and products in at least one of the LOB such as credit cards, mortgage, deposits, loans or wealth management etc.
• Experiences in AI/ML model development using cloud technologies (GCP, Azure, AWS). GCP experience with AutoML, VertexAI, etc. will be huge bonus.
• Hands on knowledge of Knowledge Graph and graph models will be a big plus
• Strong publication record in peer reviewed academic journals and/or proven track record of managing industry-academia partnership