Senior Manager, Data Science

Key Skills

Aartificial intelligence, Algorithms, AWS, AWS Sagemaker, Data science techniques, Deep Learning, Docker, Flask, Keras software library, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, REST API, TensorFlow

Job Description

Consults with business and technology partners to identify priorities and establish data analytic goals. Translates use cases to Artificial Intelligence (AI), Machine Learning (ML), and analytics — data, algorithms, and validation strategy. Drives data identification, collection, and qualification activities. Leads efforts to identify signals in data that address use cases directly or can be leveraged by analytics teams. Designs and provides critical reviews for algorithmic approaches. 

Primary Responsibilities: 

• ProvidesML leadership on complex projects, often across several business units and functions.
• Works directly with technology teams to integrate data sciencemodels into production systems. 
• Writes and delivers reports on findings for technical and non-technical audiences. 
• Providescritical reviews for algorithm designs.
• Providescore reviews for algorithm implementations.
• Conducts research and publications for the technical community. 
• Assesses data and algorithm design recommendations from data scientists and recommendschanges to larger and more complex systems.
• Defines data and model governance practices to operationalize standards. 
• Formulates mathematical and simulation models of problems,relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.
• Performs validation and testing of models to ensure adequacy andreformulate models.
• Manages the formulation of mathematical modeling and optimizingmethods to develop and interpret information that assists management with decision making, policy formulation, or other managerial functions.
• Collects and analyzes data and develops decision support software, service, or products. 
• Develops and supplies optimaltime, cost, or logistics networks for program evaluation, review, or implementation.
• Elicits, captures, and interprets customer problems from multiple perspectives across projects or within a program. 
• Establishesand project manages analysis plans for multiple complex work steams.
• Manages tothe estimation, planning, analysis, design, and development of projects.
• Holds accountability forintegration into larger, multi-disciplined projects, as appropriate.
• Leads and oversees ML strategy and road map planning. 
• Works across teams to influence and lead the direction ofexternal team. 

Education and Experience: 

Bachelor’s degree (or foreign education equivalent) in Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and five (5) years of experience in the job offered or five (5) years of experience researching and building scalable Artificial Intelligence (AI) solutions using Machine Learning (ML) and Natural Language Processing (NLP) models and technologies to improve customer experience and drive business results.

Or, alternatively, Master’s degree (or foreign education equivalent) in Analytics, Data Science, Advanced Computer Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, or a closely related field and three (3) years of experience in the job offered or three (3) years of experience researching and building scalable Artificial Intelligence (AI) solutions using Machine Learning (ML) and Natural Language Processing (NLP) models and technologies to improve customer experience and drive business results. 

Skills and Knowledge: 

Candidate must also possess: 

• Demonstrated Expertise (“DE”)performing advanced statistical analytics to develop and evaluate supervised and unsupervised ML algorithms — Regression, Decision Trees, Neural Networks, Feature Selection, Hyper-Parameter tuning, and ranking models — using Python and ML libraries (scikit-learn, Tensorflow, Keras, or PyTorch). 
• DEdesigning and developing NLP solutions to process unstructured and semi-structured text for NLP tasks – Named Entity Extraction (NER), intent detection, classification, or clustering –using classical NLP and ML methods (Deep Learning (DL) and embeddings). 
• DElaunching ML and DL models in a production environment and performing data and runtime profiling of the solutions to assess the efficacy of the ML and AI algorithms. 
• DE refactoring production-level code to achieve greater run-time performance and low latency; prototyping and deploying ML solutions using Docker-containers and Cloud-basedenvironments (Amazon Web Services (AWS) servers or AWS SageMaker); and building service end-points, using REST API and Flask.