Principal Machine Learning Engineer

Key Skills

Aartificial intelligence, Apache Airflow, Apache Hadoop, Apache Kafka, AWS, Big Data Technology, ETL/ELT technologies, Git, Luigi, Machine learning techniques, PyTorch, Snowflake, TensorFlow, Unix Operating system, XGBoost

Job Description

Position Description:

Builds, maintains, and oversees internal Python packages used to support the Amazon Web Services (AWS) workload utilized by internal teams. Acts as a technical lead in root cause analysis on internal and external data using Big Data Machine Learning (ML) toolkits — SparkML and Amazon SageMaker. Manages applications in Cloud using AWS and Cloud Formation. Writes code with Object-Oriented and object function scripting languages — Python, Java, and Scala. Performs data mining using Artificial Intelligence (Al) and ML techniques — clustering, decision tree learning, and neural networks. Builds ML models and applies complex statistical techniques to perform complex data analysis using Big Data tools — Spark, Hadoop, Kafka, Hive, and Impala.

Primary Responsibilities:

• Identifies and solves complex problems used to clarify management objectives.

• Supervises multiple, concurrent, complex projects.

• Mentors and guides junior team members.

• Presents the results of mathematical modeling and data analysis to senior management or end users.

• Leads projects in the organization and ensures successful implementation of chosen problem solutions.

• Integrates new systems with existing warehouse structure and refines system performance and functionality.

• Recommends development testing tools and methodologies and reviews and validates test plans.

• Establishes full project life cycle plans for complex projects across multiple platforms.

• Responsible for meeting project goals on-time and on-budget.

• Advises on risk assessment and risk management strategies for projects.

• Plans and coordinates project schedules and assignments for multiple projects.

• Acts as a primary liaison for business units to resolve various project/technology issues.

• Advises senior management on technical strategy.

• Performs independent and complex technical and functional analysis for multiple projects supporting several divisional initiatives.

• Develops original and creative technical solutions to on-going development efforts.

• Plans and outlines the pipeline technical-stack for the full life cycle of new product.

• Leads annual data-driven evaluation process.

• Reviews ML models deployed by junior team members.

• Monitors intra-team cross functional cooperation. Identifies business opportunities for the enterprise.

Education and Experience:

Bachelor’s degree (or foreign education equivalent) in Data Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and five (5) years of experience in the job offered or five (5) years of experience designing and developing complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.

Or, alternatively, Master’s degree (or foreign education equivalent) in Data Science, Computer Science, Engineering, Information Technology, Information Systems, Mathematics, Physics, or a closely related field and three (3) years of experience in the job offered or three (3) years of experience designing and developing complex and scalable Artificial Intelligence (AI) pipelines to improve customer experience and drive business results in the financial services industry.

Skills and Knowledge:

Candidate must also possess:

• Demonstrated Expertise (“DE”) identifying business opportunities using time series analysis, statistical, and predictive models (ARIMA, regression, classification, simulation, dimension reduction, natural language processing, and deep learning); and developing and evaluating machine learning models on large scale datasets—using scikit-learn, PyCaret, XGBoost, PyTorch, and TensorFlow.

• DE performing explorative data analysis and model building on internal and external data and processes–to solve business issues and identify areas for improvement– using Pandas, NumPy, and Plotly.

• DE performing data transformations and feature engineering using BigData tools– Hadoop, Kafka, Hive, Impala, and Snowflake; and developing Extract, Transform, and Load (ETL) data pipelines using Luigi and Airflow.

• DE deploying and managing applications both on-premise and in the Cloud– using Amazon Web Services (AWS), Hadoop, UNIX platforms, Python scripts, Git, Cron Jobs, Docker images, and Kubernetes.