Data Scientist/Machine Learning Engineer- New York, NY

Key Skills

Java Programming, Python Programming, MongoDB, SQL, Big Data Technology, Apache Hadoop, Machine learning techniques, Data science techniques, Deep Learning, Natural Language Processing (NLP), Tableau, Apache Hive, Naive Bayes, R Programming, Algorithms, SPARK Programming, Storm, Streaming APIs, Data pipelines, Large data sets, VR/AR frameworks

Job Description

Responsible for designing and developing complex requirements to accomplish business goals. Ensures that software is developed to meet functional, non-functional, and compliance requirements. Ensures solutions are well designed with maintainability/ease of integration and testing built-in from the outset. Possess strong proficiency in development and testing practices common to the industry, and have extensive experience of using design and architectural patterns. At this level, specializations start to form in either Architecture, Test Engineering or DevOp. Contributes to story refinement/defining requirements. Participates and guides team in estimating work necessary to realize a story/requirement through the delivery lifecycle. Performs spike/proof of concept as necessary to mitigate risk or implement new ideas. Codes solutions and unit tests to deliver a requirement/story per the defined acceptance criteria and compliance requirements. Utilizes multiple architectural components (across data, application, business) in design and development of client requirements. Assists team with resolving technical complexities involved in realizing story work. Designs/develops/modifies architecture components, application interfaces, and solution enablers while ensuring principal architecture integrity is maintained. Designs/develops/maintains automated test suites (integration, regression, performance). Sets up and develops a continuous integration/continuous delivery pipeline. Automates manual release activities. Mentors other Software Engineers and coaches team on CI-CD practices and automating tool stack. Individual contributor.

Global Banking Technology - (GBT):

• Believes diversity makes us stronger so we can reflect, connect and meet the diverse needs of our clients and employees around the world
• Is committed to building a workplace where every employee is welcomed and given the support and resources to perform their jobs successfully
• Wants to be a great place for people to work and strives to create an environment where all employees have the opportunity to achieve their goals
• Provides continuous training and development opportunities to help employees achieve their career goals, whatever their background or experience
• Is committed to advancing our tools, technology, and ways of working to better serve our clients and their evolving business needs
• Believes in responsible growth and is dedicated to supporting our communities by connecting them to the lending, investing and giving them what they need to remain vibrant and vital

Responsibilities:

• Design and develop scalable ML/AI solutions to solve diverse business challenges by deriving features from rich data sources, training, evaluating and deploying models to production using cutting edge technologies
• Gather and analyze data to perform statistical analysis, identify key factors and build comprehensive visualizations to report findings
• Utilize statistical methods to process, clean and validate data for uniformity and accuracy
• Create and maintain end-to-end data pipelines and APIs according to business requirements
• Communicate analytic solutions to stakeholders and implement improvements as needed to operational systems

Required Skills:

• 5+ years in a data science role with proven record of implementing end-to-end ML/AI solutions into production
• 5+ years of experience using statistical computer languages, such as R or Python (preferred)
• 3+ years of experience working with large data sets (> 1TB) and using big data solutions such as Hadoop, Hive, Spark, Storm, MongoDB etc.
• 1+ year of experience with deep learning (e.g., CNN, RNN, LSTM) and NLP frameworks
• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, etc. and their real-world advantages/drawbacks
• Rigorous understanding of statistics and ability to discern appropriate statistical techniques to problem-solve
• Proficiency with writing SQL queries

Desired Skills:

• Master’s degree or PhD in computer science, applied mathematics, or related technical/scientific field
• Experience with data visualization tools, such as Tableau
• Prior work experience in the financial industry