Position Description:

Designs and implements Machine Learning (ML) and Deep Learning (DL) algorithm approaches in multiple projects. Programs Machine Learning frameworks using Python and R. Develops and models software solutions using Natural Language Processing (NLP), Information Retrieval, Machine Comprehension, Question Answering/Conversational Artificial Intelligence (AI), Reinforcement Learning, Knowledge Graph, Causal Inference, and Design of Experiment. Conducts exploratory data analysis according to measurements, unstructured data analysis, predictive analytics, and prescriptive analytics using Big Data, NLP, and chatbot technologies (Elasticsearch and Solr). Encourages ML and DL using TensorFlow, Keras, MXNET, and H2O.

Primary Responsibilities:

• Develops supervised and unsupervised Machine and Deep Learning algorithms – regression, classification, decision trees, neural networks, and clustering.

• Researches and builds complex, cutting edge and scalable AI algorithms by fine-tuning language models to deliver high value AI/ML solutions.

• Creates business and technical requirements.

• Works across teams and influences the direction of external teams.

• Defines data requirements and gathers and validates information.

• Performs validation and tests of models to ensure adequacy and reformulates models as necessary.

• Applies principles, techniques, procedures, and technologies to the design and production of various products.

• Collaborates with others to resolve information technology issues.

• Deliver oral or written presentations of the results of mathematical models and data analysis to management or other end users.

• Analyzes, manipulates, or processes large sets of data using statistical software.

• Sets a strategic direction for data identification, collection, and qualification activities.

• Leads data analysis for multiple projects with diverse scope and complex business and technical challenges across several business units and functions.

• Coordinates and guides data science and data engineering elements of AI projects and ML techniques.

• Implements new technologies in a production environment with product, IT, and data engineering teams.

• Presents reports and findings to senior level technical and non-technical audiences.

• Develops and applies mathematical or statistical theory and methods.

• Collects, organizes, interprets, and summarizes numerical data to provide usable information.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and five (5) years of experience as a Senior Manager, Data Science (or closely related occupation) building algorithms to deploy applications in a financial services environment, using programming languages and Machine and Deep Learning frameworks.

Or, alternatively, Master’s degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and two (2) years of experience as a Senior Manager, Data Science (or closely related occupation) building algorithms to deploy applications in a financial services environment, using programming languages and Machine and Deep Learning frameworks.

Or, alternatively, PhD degree (or foreign education equivalent) in Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and no experience.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) performing predictive modelling to develop, train, and evaluate supervised and unsupervised Machine Learning (ML) algorithms – Regression, Classification, Clustering, Decision Trees, Neural Networks, Feature Selection and Hyper-Parameter tuning – using Python, Machine and Deep Learning frameworks (scikit-learn, TensorFlow, PyTorch, or Keras) with use cases in Conversational AI and Search applications.

• DE designing and developing NLP and Natural Language Understanding (NLU) solutions -- Conversational AI, Information Retrieval, Question-Answering systems, Named Entity Extraction (NER), Summarization, and Text classification – using NLP, ML, DL, and embeddings methods (Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT)).

• DE applying and training open-source generative Large Language Models (LLMs) -- Instruction fine-tuning, Direct Preference Optimization (DPO), evaluating, prompt engineering, prompt-tuning, large scale synthetic data generation, and productionizing generative LLMs for Conversational AI use-cases -- using Python, Transformers, DL frameworks (TensorFlow, PyTorch, or scikit-learn), and Cloud Platforms (Amazon Web Services (AWS)) for training and hosting models.

• DE writing production-grade quality, scalable, and efficient code to implement algorithms, using Python in a production environment; and refactoring production-level code to achieve run-time performance and low latency, using optimization techniques (quantization and knowledge distillation).

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