Fidelity

Job Description:

Position Description:

Develops and deploys Machine Learning (ML) enabled products and services at scale using Deep Learning (DL) methodologies related to document automation, computer vision, and natural language models. Solves business problems — Business to Consumer (B2C), Business to Business (B2B), quality operations, operations management, and automation — using Artificial Intelligence (AI) and data science techniques (Natural Language Processing (NLP), DL, ML, causal inference, predictive analytics, experimental design, and optimization). Leads a team of data scientists, collaborates closely with data engineers, system integration engineers, quality engineers, and business stakeholders to solve challenges, text analytics problems, with innovative solutions. Owns the delivery of AI-enabled applications. Provides analytic consults to the business and identifies and gathers complex data from multiple sources. Conducts experiments to test algorithms, monitor model performance, interpret findings, and present work to technical and non-technical audiences.

Primary Responsibilities:

• Performs applied research in ML, computer vision, recommender systems, NLP, time series models, transformers, graphs and their applications in search, and personalization and marketing.
• Leads and manages high performance data science teams and delivers industry leading AI-enabled products and services.
• Leads and implements document processes, computer vision, and DL.
• Leads and deploys projects involving large scale multi-dimensional databases, complex business infrastructure, and cross-functional teams.
• Leads the design and development of innovative solutions for customer and associate-centric projects -- text mining, document/image processing, and document-processing workflows.
• Implements new technologies in production environments.
• Guides data science and engineering teams to deliver projects and capabilities at scale.
• Develops and implements a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software.
• Applies data mining and modeling, NLP, and ML techniques to extract and analyze information from large structured and unstructured datasets.
• Visualizes, interprets, and reports data findings.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Applied Mathematics, Computer Science, Engineering, Information Technology, Information Systems, Information Management, Mathematics, or a closely related field and five (5) years of experience as a Director, Data Science (or closely related occupation) launching, operating, leading and implementing document processing, computer vision, and Deep Learning (DL) practices using TensorFlow, Keras, MXNET, or H2O.

Or, alternatively, Master’s degree (or foreign education equivalent) in Applied Mathematics, Computer Science, Engineering, Information Technology, Information Systems, Information Management, Mathematics, or a closely related field and two (2) years of experience as a Director, Data Science (or closely related occupation) launching, operating, leading and implementing document processing, computer vision, and Deep Learning (DL) practices using TensorFlow, Keras, MXNET, or H2O.

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

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) developing semi-supervised, supervised, and unsupervised Machine Learning (ML) (in Python) and Deep Learning (DL) models (in PyTorch); applying functional paradigms -- Haskell -- to build composable learning systems and products; and performing probabilistic modeling, using PyStan, PyMC3, and Pyro.
• DE researching, applying, and deploying state-of-the-art Natural Language Processing (NLP) methods, using Python and DL frameworks -- PyTorch; and performing model compilation and optimization using TorchScript and ONNX.
• DE performing advanced statistical analytics to develop, analyze, and evaluate supervised and unsupervised ML algorithms -- Regression, Decision Trees/Random Forest, Neural Networks, Feature Selection/Reduction, Clustering, Hyper-Parameter tuning, marketing attribution models, and treatment control matching -- using Python, C++, Java, Tensorflow, Keras, PyTorch, and Spark and scikit-learn -- with specific use cases on Recommender Systems and Reinforcement Learning/Bandits applications (within a planning, advice, and financial investments domain).
• DE contributing and designing enterprise architecture financial platforms, using highly available, scalable, fault tolerant, performance efficient, and resilient REST APIs and event based scalable frameworks using Amazon Web Services (AWS), Java/J2EE, NodeJs, Python, API Gateway, and Continuous Integration/Continuous Delivery (CI/CD) pipelines.

[Expertise may be gained during doctoral program].

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Certifications:

Category:

Data Analytics and Insights

Fidelity’s working model blends the best of working offsite with maximizing time together in person to meet associate and business needs. Currently, most hybrid roles require associates to work onsite all business days of one assigned week per four-week period (beginning in September 2024, the requirement will be two full assigned weeks).