Director, Data Science Online

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.
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.
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.
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.
Demonstrated Expertise (“DE”) developing semi-supervised, supervised, and unsupervised Machine Learning (ML) (in Python) and Deep Learning (DL) models (in PyTorch); DE researching, applying, and deploying state-of-the-art Natural Language Processing (NLP) methods, using Python and DL frameworks -- PyTorch; 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.
[Data Analytics and Insights Job 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.
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.
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.
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.
Demonstrated Expertise (“DE”) developing semi-supervised, supervised, and unsupervised Machine Learning (ML) (in Python) and Deep Learning (DL) models (in PyTorch); DE researching, applying, and deploying state-of-the-art Natural Language Processing (NLP) methods, using Python and DL frameworks -- PyTorch; 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.
[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. At Fidelity, we are passionate about making our financial expertise broadly accessible and effective in helping people live the lives they want For information about working at Fidelity, visit FidelityCareers.com.
Fidelity Investments is an equal opportunity employer.
Fidelity will reasonably accommodate applicants with disabilities who need adjustments to participate in the application or interview process.